# Advancing High-Throughput Cellular Atomic Force Microscopy with Automation and Artificial Intelligence

Ophélie Thomas- -Chemin, Sébastien Janel, Zeyd Boumehdi, Childérick

Séverac, Emmanuelle Trevisiol, Etienne Dague, Vincent Duprés

► To cite this version:

Ophélie Thomas- -Chemin, Sébastien Janel, Zeyd Boumehdi, Childérick Séverac, Emmanuelle Trevisiol, et al.. Advancing High-Throughput Cellular Atomic Force Microscopy with Automation and Artificial Intelligence. ACS Nano, 2025, 19 (5), pp.5045-5062. 10.1021/acsnano.4c07729. hal-04956507

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Distributed under a Creative Commons Attribution 4.0 International License
Advancing high-throughput cellular Atomic
Force Microscopy with automation &
artificial intelligence

Ophélie Thomas-Chemin<fnref n="1" />, Sébastien Janel<fnref n="2" />, Zeyd Boumehdi<fnref n="1" />, Childérick Séverac<fnref n="1" />,<fnref n="3" />, Emmanuelle Trevisiol<fnref n="1" />,<fnref n="4" /> Etienne Dague<fnref n="1" />*, Vincent Duprés<fnref n="2" />*

<footnote n="1">¹: LAAS-CNRS, CNRS, Université de Toulouse, 31400 Toulouse, France</footnote>

<footnote n="2">²: Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 9017 - CIIL - Center for Infection and Immunity of Lille, F-59000 Lille, France</footnote>

<footnote n="3">³: RESTORE Research Center, Université de Toulouse, INSERM, CNRS, EFS, ENVT, Université P. Sabatier, 31100 Toulouse, France</footnote>

<footnote n="4">4 : TBI, Université de Toulouse, CNRS, INRAE, INSA, 31400 Toulouse, France</footnote>

<footnote>*: authors who have equally contributed to the work and to whom correspondence should be addressed, edague@laas.fr, vincent.dupres@ibl.cnrs.fr</footnote>
**Abstract**

Atomic force microscopy (AFM) has reached a significant level of maturity in biology, demonstrated by the diversity of modes for obtaining not only topographical images but also insightful mechanical and adhesion data by performing force measurements on delicate samples with a controlled environment (e.g., liquid, temperature, pH). Numerous studies have applied AFM to describe biological phenomena at the molecular and cellular scales, and even on tissues. Despite these advances AFM is not established as a diagnostic tool in the biomedical field. This article describes the reasons for this gap, focusing on one of the main weaknesses of bio-AFM: its low data throughput. We review current efforts to improve the automation of AFM measurements in particular on living cells, as well as the developments in automating data analysis. For this latter, artificial intelligence (AI) is progressively employed to classify data to distinguish healthy and diseased cells or tissues. Finally, we propose a roadmap to foster the application of bio-AFM into medical diagnostics.

**Keywords:**

1. Atomic force microscopy (AFM)

2. High-throughput AFM

3. Bio-AFM

4. Cellular analysis

5. Artificial intelligence (AI)

6. Automation

7. Mechanical properties

8. Machine learning

9. AFM data processing

**Vocabulary:**

- **Cellular high-throughput AFM:** Cellular high-throughput AFM refers to the ability to analyze a large number of cells efficiently within a given timeframe using atomic force microscopy. This approach emphasizes technological advancements, such as automation and optimized measurement techniques, to increase the number of cells analyzed while maintaining precision and relevance in biological studies.

- **AFM automation:** AFM automation refers to the use of advanced technologies to streamline and optimize atomic force microscopy processes, including sample
preparation, measurement acquisition, and data analysis. This reduces manual intervention, increases throughput, and enables consistent and high-resolution analyses of a large number of samples or cells, making AFM more efficient and scalable for various applications.

- **AI for AFM:** AI for AFM involves the application of artificial intelligence techniques, such as machine learning and deep learning, to enhance atomic force microscopy processes. It is used for tasks like automating image and data analysis, improving measurement accuracy, and classifying biological samples, enabling faster and more reliable insights into complex structures and properties.

- **Artificial Intelligence (AI):** AI encompasses all methods and techniques used to imbue computers or machines with intelligence.

- **Machine Learning (ML):** ML is a subset of AI, focusing on algorithms that enable systems to learn from data without explicit programming.

- **Deep Learning (DL):** within ML, DL stands out for its utilization of neural networks, which are structured to mimic the way the human brain processes information, allowing for more complex learning and decision-making capabilities.
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# TOWARD HIGH THROUGHPUT CELLULAR ATOMIC FORCE MICROSCOPY

*Graphical abstract – Road map toward High Throughput Cellular AFM*
# 1. Introduction

In 1982, Binnig and coworkers⁵ developed a technology capable of measuring the tunnel current between a tip and a conductive sample. They confirmed that the intensity of the tunnel current is a function of the distance between the tip and the sample, and came up with the idea of using this property to build the scanning tunneling microscope. To keep the value of the tunnel current constant during the x,y scan of the sample, the vertical position (z) of the tip must be continuously adjusted, enabling the topography of the sample to be reconstructed. A few years later, Binnig and Quate used the same idea, but made the height of the tip dependent not on the tunnel current, but on the force applied by the tip - the atomic force microscope (AFM)⁶. This development has many advantages, such as the ability to analyze nonconductive samples, to work in air or even in liquid, and to measure forces. This set of advantages has made it possible to analyze biological samples, from biomolecules, bacteria, yeast, to eukaryotic cells and even tissues⁷⁻¹⁴.

AFM for biology has enabled numerous discoveries to be made in the field of cellular and structural biology, using the imaging capabilities of biological samples as close as possible to physiological conditions, but also taking advantage of the force machine to measure mechanical properties of samples or interactions at the scale of the cell or single molecule. This work has led to the development of numerous derivative modes such as single molecule force spectroscopy (SMFS)¹⁵⁻¹⁷, single cell force spectroscopy (SCFS)¹⁸,¹⁹, chemical force microscopy (CFM)²⁰,²¹, high-speed AFM (HS-AFM)²²,²³, multiparametric AFM²⁴,²⁵, Fluid FM²⁶, etc.

## Cellular high-throughput AFM: a definition

All these impressive developments have led to major scientific discoveries. However, the question of the statistical relevance is often too poorly considered and/or described, as most of the AFM studies are based on the analysis of a limited number of cells. This is an inherent constraint of AFM, which requires a long data acquisition time per cell. It is also one of its advantages: working at the cellular or even at the molecular scale. This limitation needs to be overcome in some biomedical applications where the robustness of the results depends on a large number of cells to be assessed before finding the relevant cell or cells targeted by the assay. The term “throughput” must be clarified as it is widely used in many different contexts and as some AFM modes are themselves presented as “high-throughput” modes²⁷⁻³⁰. Multiparametric modes²⁵,³¹ have made it possible to increase the speed of force curves acquisition by optimizing the acquisition technology. They are multiparametric as they extracts numerous biophysical properties (e.g. elasticity, adhesion, dissipation, etc.) from a force curve. These modes are of interest in terms of the speed with which force curves can be obtained within the usual framework of bio-AFM (AFM close to physiological conditions), i.e., the measurement of a few cells.

On the other hand, HS-AFM modes have been developed and can be confused with high-throughput. HS-AFM enables AFM imaging at video rates. This major advance has made it possible to image molecules at work, the proof of concept being the
myosin molecule walking on an actin fiber<sup>22,32</sup>. This mode produces large quantities of images over a very small area but does not adapt well to cells. The throughput in terms of the number of images is high, but very low in terms of samples.

Another example of an application seeking to increase throughput is JPK Bruker's Force Robot. This tool is presented as a high-throughput, single-molecule, quantitative biomechanics technology. The Force Robot can automatically generate up to 250,000 force curves per day, and provides advanced curve analysis tools. However, this tool only probes one type of interaction, at the single-molecule level, on a single sample.

This brief analysis shows that throughput in terms of images or force measurements means multiplying the number of images or measurements on a small number of cells. In this article, we consider the term 'high-throughput' on the biological sample side, i.e. a large number of cells (around 1000) analyzed in a given time and focus on technological developments enabling the number of cells analyzed to be increased. These developments mainly concern the automation of AFM measurements, which imposes a certain number of constraints in terms of sample preparation, also enables the analysis of large quantities of data using artificial intelligence tools.

Looking in the field of biology, several “high-throughput” techniques can be used to analyze a large number of cells in a short period of time. This is the case of microfluidics for drug-screening applications or flow cytometry imaging, which can process up to 50,000 cells per second³³. Given the specificities of an AFM such numbers are not to within reach. Nevertheless, analyzing a hundred cells during an experiment would be a major step forward for a so-called “high-throughput” AFM experiment. AFM would still offer the advantage of being more versatile, offering higher resolution imaging or analysis of several mechanical properties.

The emergence of AFM automation and data analysis with the help of AI in the context of bio-AFM

Figure 1 attempts to put the two different development paths that are automation in red and artificial intelligence in blue into perspective, by comparing the high-throughput AFM for materials and cellular AFM that is now emerging.

<figure><img src="image_3.png" /><figcaption>**Figure 1** – Research developments in AFM and artificial intelligence. Number of publications as a function of time for the word combinations AFM + Automation (blue) and AFM + Cell + Automation (red) (A), AFM + Artificial intelligence (blue) and AFM + Artificial intelligence + Automation (red) (B) and AFM + Cell + Artificial intelligence (blue) and AFM + Cell + Artificial intelligence + Automation (red) (C). Histograms were generated on Dimensions with the Title and abstract category.</figcaption></figure>
A publication mentioning AFM automation appeared as early as 1996<sup>34</sup> (blue part of Fig. 1A), in the semiconductor community. In this field, AFM is used to characterize the thickness and sidewall profile of micro-structured silicon wafers, but it is notoriously slow. To be able to improve the throughput, the authors had already considered the need to automate the tool. But the number of scientific papers dealing with AFM automation is relatively low, between 10 and 20 papers per year since 2014. In the 90s, this was not an issue in the bio-AFM community, which was busy proving the concept of the applicability of AFM in biology<sup>35</sup>. The proof of concept for the applicability of AFM in biology dates back to 1994<sup>35</sup>. The authors examined the activation of live native human platelets under physiological conditions using AFM images showing the distribution of cytoskeletal components and membrane-bound filaments. In this field, the automation of AFM on cells only began in 2006, but its application has remained very limited, with 3-4 publications per year since then (red part of Fig. 1A). It was not until 2008 that a paradigm shift took place and the term bio-AFM appeared<sup>36</sup>. From 2008 onwards, equipment began to acquire the ability to regulate temperature and pH. These advances enabled researchers to faithfully imitate the environment of living cells. They could thus observe the resulting changes in behavior over time or in response to environmental variations. In the publication by Chang *et al*.<sup>36</sup>, the authors used bio-AFM to characterize surface morphological changes in expanded poly(tetrafluoroethylene) membranes grafted or not with PEGMA macro-monomers. The use of bio-AFM enabled them to carry out this characterization in the hydrated state and they were able to find differences in the morphology of the grafted membrane between this hydrated state and a dry state measured by SEM.

At the same time, articles dealing with artificial intelligence and AFM (Fig. 1B) appeared in 2002³⁷. In this study, the authors explored the use of genetic algorithms for the restoration and processing of images obtained by AFM, thereby improving the quality of these images for analyzing the structures of samples. These works remained fairly isolated until 2015 (2-3 publications per year) when publications became more numerous, reaching more than 60 articles each year by 2023 (blue part of Fig. 1B). The trajectory seems exponential and could well remain so, given the current craze for artificial intelligence-related subjects. Strangely, there is no link with the field of automation, which we might have thought necessary to generate the large quantities of data that artificial intelligence requires (red part of Fig. 1B). (See didactic box #1 for an overview of what is AI including machine learning and deep learning).

In 2012, AFM data in the field of biology was analyzed using machine learning algorithms<sup>38</sup> (blue part of Supplementary data 2 B). The authors developed a pipeline to process AFM images using a simple linear regression model, bi-weighted by a Beaton-Tukey function, to estimate cDNA backbone lengths with no lower bounds that might be due to pixel density, allowing them to have a resolution of order 20-25 base pairs (bp). In this way, they were able to calculate 15 distinct theoretical fragment lengths ranging from 66.00 to 588.10 nm with a percentage error of between 0.28 and 3.24 %. However, the use of AI in bio-AFM only emerged in 2018 and is growing year on year, with more than 20 publications by 2023. As in the field of materials, there is no real increase in the use of automation coupled with AI (red part of Fig. 1B).
To sum up, the aim of this review is to bring together the work that has contributed to the development of cellular high-throughput AFM. In the first chapter, we discuss methods for sample preparation (cell immobilization) and AFM preparation and calibration. These steps need to be compatible with living cells, and also enable measurement automation, which is often a challenge in its own right. The second chapter deals with the multiplexing of measurements or their automation to acquire data on a large number of cells. Finally, the third chapter looks at data processing, bringing together the literature that has already used artificial intelligence to classify bio-AFM data. The conclusion of the article places these elements on a chronological timeline and proposes a roadmap for the coming years for the developments still to come.

# 2. Setting up the Experiment

## 2.1. Sample preparation

### 2.1.1. Adhesion & structuration of the sample

Sample preparation for AFM scanning is highly dependent on the sample itself and must be adapted to its adhesion and size properties.

Some samples are self-adherent and occupy the surface completely (e.g., cell monolayers, large tissue, supported lipid bilayer). In this situation the AFM can automatically engage and scan.

Some biological samples are adherent but don't occupy all of the surface (e.g., isolated eukaryotic or prokaryotic cells). In this case objects can be detected either by optical means³⁹ or by a quick AFM survey scan that can be followed by a higher resolution scan¹ using the closed-looped scanner and, if needed, motorized stage repositioning (Fig. 2A-D).

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**Figure 2** – Sample preparation. Multi-well plate (A) in which Yersinia pseudotuberculosis bacteria are seeded (B, C) for AFM measurements on single bacteria (D). Adapted from¹. Image of a yeast array composed of an array of micro-structured PDMS wells seeded with C. albicans (E). Adapted from². Fluorescence images of PC3-GFP cells adhered to an array of fibronectin patterns forming a cell array (F). Adapted from⁴⁰. Parallel AFM used to image multiple locations on a large surface sample such as a wafer or mask (G, H). Adapted from⁴¹. Fig. 2A-D was adapted with permission under a Creative Commons License from CC BY 4.0 ATTRIBUTION 4.0 INTERNATIONAL (https://creativecommons.org/licenses/by/4.0/). Copyright 2019/ Dujardin, A.; Wolf, P. D.; Lafont, F.; Dupres, V. PLOS ONE. Fig.2F was adapted with permission under a Creative Commons License from
CC BY 4.0 ATTRIBUTION 4.0 INTERNATIONAL (https://creativecommons.org/licenses/by/4.0/). Copyright 2018/ Foncy, J.; Estève, A.; Degache, A.; Colin, C.; Dollat, X.; Cau, J.-C.; Vieu, C.; Trévisiol, E.; Malaquin, L. PLOS ONE. Fig.2G was adapted with permission under a Creative Commons License from CC BY 4.0 ATTRIBUTION 4.0 INTERNATIONAL (https://creativecommons.org/licenses/by/4.0/). Copyright 2017/ Sadeghian, H.; Herfst, R.; Dekker, B.; Winters, J.; Bijnagte, T.; Rijnbeek, R. Rev. Sci. Instrum.

Several approaches have been developed to optimize the positioning of single cells on surfaces<sup>42</sup>. In the case of prokaryotic cells, one method is printing either by convective/capillary deposition on SiO<sub>2</sub> surface<sup>43</sup>, or by designing APTES-functionalized spots<sup>44</sup>. Soft lithography strategy consists in constricting cells in a PDMS microstructure of controlled size and chemistry to help them withstand forces exerted during scanning<sup>2,45,46</sup> (Fig. 2E). Microfluidic-based deposition seems a very promising approach<sup>47,48</sup> and would benefit further developments for the automatic positioning and discarding of individual cells. Eukaryotic cells have also been immobilized on defined substrate geometries, either by soft lithography microcontact printing<sup>49-52</sup> (Fig. 2F), ECM microarrays<sup>53</sup>, or UV exposure patterning with a chrome photomask<sup>54,55</sup> or with a more versatile, dedicated instrument (Alvéole Lab PRIMO<sup>56</sup>). Lastly, fluidFM technology can be automated to print adhesion molecules at given positions<sup>57,58</sup> and also to structure soft hydrogels that mimic the cell environment<sup>59</sup>. Non-adherent cells must be immobilized to the surface either by chemical functionalization (e.g., poly-l-lysine, APTES, polydopamine) or protein functionalization of the surface (e.g., ECM proteins, conA). It has been shown that it is possible to capture and release circulating eukaryotic cells by magnetism control<sup>60</sup>.

### 2.1.2. Sample holder

There can be many requirements and constraints depending on the sample type. Prokaryotes might simply be imaged in air at ambient temperature but most eukaryotes need liquid medium at physiological conditions. When heating the sample at 37 °C, evaporation will occur with a total drying out of the sample in a few hours. Furthermore, subtle changes in the pH or salt concentration influence cell behavior, so these parameters must stay as stable as possible. This makes medium circulation/exchange necessary for long-time automation. In the case of correlative microscopy, glass-bottom Petri dishes (with standard sizes) must be used for optical transparency purposes. In practice, users mainly rely on sample holders developed by AFM companies. Most of them provide a single Petri dish holder or liquid cell with temperature control and accessories for fluid exchange and/or CO₂ environment. Unfortunately, these are not suitable for multi-sample automation. Dujardin and coworkers¹ developed a 12-well prototype plate with fluid reservoir and clamping mechanism to immobilize standard round glass coverslips on the large and open Bruker FastScan stage, though not with temperature nor CO₂ control. Building a multi-well sample holder with temperature, CO₂, and evaporation control is the next step toward live cell automation. Another current limitation is the bulkiness of the AFM probe holder relative to the size of commercial multi-well plates. By using a competing technique to AFM (for mechanical measurements only), Optics11 Life has developed a system (i.e., Pavone) that is compatible with standard 96-well plates with temperature, CO₂, and humidity control. Another meaningful benefit of using standard well plates is the possibility to correlate AFM with high-content analysis microscopy (HCA).
## 2.2. Microscope & tip preparation

### 2.2.1. Microscope selection

The type of AFM microscope used for automation is dictated by the biological question. The main issue is whether a standalone AFM will be able to detect where to scan or if photonic/correlative microscopy with image analysis is needed for the identification and detection of the sample.

Another point to consider is the stability of the microscope. It must keep the laser optically aligned with the cantilever for hours without any user intervention. A welcome feature is the automatic positioning of the laser and the automatic alignment of the photodiode with internal motors (e.g., Asylum Research Cypher, Bruker FastScan Bio, Bruker NanoWizard V, Park systems FX40). This can be automatically run at regular intervals, when the laser photosum changes, and when the laser is not centered. This is especially crucial when force measurements are performed to get a proper force detection system.

Importantly, the microscope must provide state-of-the-art components for good measurements such as X-Y-Z piezo sensors and motorized stage with encoders for precise and repetitive displacements, as well as access to low-level instrumental functions via application programming interfaces (APIs) or scripting capabilities.

### 2.2.2. Tip mounting

Automated AFM tip mounting is a difficult task to accomplish because of the large diversity of probes shapes and sizes used for biological applications. Vikrant et al. have designed a setup to attach and detach a tip from a tipless cantilever using a laser heating a wax microsphere with a visual servo command⁶¹. In another approach, Kumar and colleagues have developed a technique of microfabrication and focused ion beam etching to make an AFM tip cartridge where probes ("Nanobits") can be picked up and released as needed following a robotic sequence⁶².

Park systems FX40 microscope can automatically mount and align the probe by magnetically picking it up from a proprietary 8-probe cassette, with QR code identification of the model.

### 2.2.3. Calibration

Considering the AFM microscope has built-in displacement sensors, possibly working in closed loop, the only calibration that must be run for each experiment are the InvOLS (Inverse Optical Lever Sensitivity or "deflection sensitivity") and the spring constant of the cantilever (k). The classical so-called thermal noise method consists in making a force curve on an infinitely stiff surface to get InvOLS, and a thermal tune acquisition to get k. The Sader method is based on the geometry and material properties of the cantilever, only applicable to rectangular cantilevers⁶³. This has been further developed for any shape⁶⁴, and with Sader Global Calibration Initiative (GCI) for sharing thermal results within the community⁶⁵. This has been refined for low Q cantilevers in liquid to provide a one-step calibration method⁶⁶. The SNAP approach⁶⁷ is based on the precise a priori measurement of the spring constant by laser Doppler velocimetry (LDV)⁶⁸ and the determination of the InvOLS by a thermal tune, eliminating large uncertainties when performing InvOLS determination with a force curve. Besides a better calibration, SNAP calibration method allows for a
simple, regular, automated calibration of the InvOLS through the acquisition of a simple thermal tune, without having to touch the surface with the probe. To get a true InvOLS value this should be performed regularly in order to compensate for every little drift happening because of temperature and mechanical drifts. Some no-touch and touch calibrations were compared to NIST electrostatic force balance⁶⁹. Other calibration methods not applicable to automation in bio (to date) are reviewed in detail by Palacio et al⁷⁰. In the future, interferometric AFM might solve optical lever calibration issues by measuring the true tip displacement (e.g., Oxford Instruments Vero). Still, the measured value is highly dependent on the position of the laser spot at the backside of the cantilever and therefore the calibration must be executed with great care exactly above the tip, otherwise leading to inaccuracies.

A summary of the different types of calibration listed can be found in Table 1.

<table><caption>**Table 1 – Table summarizing the main calibration methods for AFM cantilevers.** For each type of calibration, we present the advantages and disadvantages, as well as whether the method is applicable to AFM automation.</caption><thead><tr><td>Calibration method</td><td>Pros</td><td>Cons</td><td>Applicable to automation</td></tr></thead><tbody><tr><td>Classical method (FD curve + thermal noise)</td><td>Widely available</td><td>InvOLS calibration is not reliable leading to erroneous k</td><td>-/+</td></tr><tr><td>Sader method</td><td>Precise, reliable No surface touch</td><td>Limited to rectangular cantilevers with known dimensions Valid for high-Q cantilevers</td><td>++</td></tr><tr><td>Sader Global Calibration Initiative</td><td>Any cantilever shapes No surface touches Community database</td><td>Partial implementation in software Assumes reliable microfabrication</td><td>++</td></tr><tr><td>SNAP (pre-calibrated cantilevers)</td><td>Precise, reliable No surface touch</td><td>Cost/access to LDV pre-calibration</td><td>+++</td></tr></tbody></table>

### 2.2.4. Scanning mode

AFM can be operated in many different imaging modes, using different feedback signals. The recently developed force feedback modes (Quantitative Imaging™, PeakForce Tapping®, etc.) are better suited for automation in biology as they are fast and provide pixel by pixel feedback. This is superior to contact or intermittent contact mode feedback where drift is not compensated and can result in a very high setpoint force after minutes/hours of scanning. Another crucial benefit of force mapping modes is that the vertical force applied by the tip on the sample is strictly controlled with no lateral movement of the cantilever during ramping and therefore the tip should not wear off, as demonstrated by Strahlendorff et al ⁷¹ on an HS-AFM setup. The scanning itself is discussed in detail in the “data acquisition” chapter.
### 2.2.5. Probe integrity & cleaning

The AFM probe is an essential part of a correct experiment. As such probe control is essential for any type of automated experiment, i.e., imaging, elasticity, and adhesion measurements. For imaging, an incorrect tip will bring artifacts (“double-tip” effect, etc.). For adhesion experiments a contaminated tip will give false positive events, often with a very similar pattern in the retract force distance (FD) curve. As importantly, a contaminated or damaged tip will bias mechanical measurements as the shape or size of the indenter will not be the one defined in the model used for fitting the data (i.e., Hertz and Hertz-derived)⁷².

In the case of adhesion experiments using tip functionalized with appropriate molecule, specific negative and positive control FD curves must be regularly performed on a dedicated sample, depending on the grafted molecule.

The cantilever can also be subject to alterations such as contamination by the sample (e.g., floating cellular debris), or formation of air bubbles when going from one liquid sample to another. A way to monitor the cantilever integrity is to record the Sum signal of the optical lever. A falling sum will alert a failing cantilever. Another possibility is to use optics to evaluate the cantilever over time using image analysis¹.

If a tip is contaminated, the automated microscope must try to clean it. A solution is to dip the probe in a cleaning bath, i.e., detergent, solvent, piranha solution⁷³, followed by at least a rinsing bath¹. Dry cleaning can be performed by UV exposure, as the alternative plasma cleaning seems hard to implement in a microscope. In the specific case of high-resolution imaging using STM, Alldritt and coworkers have developed an open-source software package that enables the automatic functionalization of the tip with carbon monoxide molecules immobilized on a copper surface⁷⁴.

If the tip is damaged, the microscope must change it automatically (see “Tip mounting” section). If the tip mounting is not automated the microscope must stop the experiment and alert the user to change it. Another approach suited for mechanical measurements would be to use the FluidFM ability to swap colloidal tips automatically from a dedicated well with colloidal suspension⁷⁵.

The actual golden standard for assessing tip shape and size, with some limitations, is to image a sample with sharp gratings and analyze the AFM data⁷⁶. Another possibility that is more dedicated to high-resolution imaging is to image a well-known sample⁷⁷ and use machine learning to assess the tip integrity by comparison with the original image. Using machine learning can be problematic as the algorithm must be retrained on a large dataset for every sample type. In this regard, Barker et al. have used template matching classification with only one STM image of repeating structures with the same success as machine learning for both assessing and maintaining the tip state⁷⁸. Imaging the tip with a scanning electron microscope⁷⁹ does not seem to be suited for automation.

To sum up, the use of local probe technologies in biology requires a high level of interdisciplinary expertise, combining physics, surface chemistry and biology. Sample preparation, for example, addresses surface chemistry, microfabrication or physical chemistry. The functionalization of tips is a question of surface chemical, while the calibration of tips and the operation of AFM modes is essentially a physical matter. Finally, the interpretation of data from a mechanical or interaction point of view is a biophysics subject. That said, it is clear that each sample, and indeed each related scientific question, requires specific developments. There is not, nor can there be, an ideal AFM capable of meeting all past and future needs. It is essential to capitalize on
the skills and potential of the engineers and researchers who operate the instruments
to push back the frontiers of knowledge whenever a challenge arises.

3. Data Acquisition

Even if AFM has proven its potential in biology over the last 3 decades, the technique
remains severely limited by its image acquisition rate and consequently the low
throughput associated. Various approaches were explored in an attempt to improve
the way in which data is acquired and then to increase the throughput and we will
review them in this section.

Obtaining an image is a process that can take up to several minutes, depending on
the resolution required, whereas this time is even longer to generate an adhesion or
elasticity map of a sample. Increasing the AFM cantilever's speed during scanning is
therefore an obvious way of reducing acquisition times and has been an area of
focus for many years, thanks to the development of high-speed AFM (HS-AFM) by
several groups⁸⁰. HS-AFM has amply demonstrated its potential, particularly in the
field of imaging, with the ability to obtain images with a nanometer resolution and
sub-second frame rate, making it possible to observe dynamic processes at single
molecule level in real-time⁸¹. These improvements have been achieved through
various enhancements in the AFM setup, such as using small specialized cantilevers,
fast scanners or fast amplitude detectors⁸². However, the current HS-AFM has
limitations, such as a small sample stage (around 1.5 mm in diameter) or the lack of
coupling with an optical microscope, which have greatly reduced applications to
larger samples such as bacteria⁸³ or mammalian cells⁸⁴. To overcome these
restrictions, recent technical advances have been made such as the development of
a tip-scanning HS-AFM combined with fluorescence microscopy⁸².

To reach higher scanning speeds on cells and tissues without having to use HS-AFM, progress has been made towards the development of scanning modes such as Quantitative Imaging (QI), Fast Force Volume and PeakForce Quantitative Nanomechanical Mapping (PF-QNM)³¹. While the first two are standard force curve - based techniques, PF-QNM operates sinusoidally the z-piezo. PF-QNM is based on PeakForce Tapping mode, which oscillates the probes at about 1 kHz while the feedback used is the peak force of the interaction between the tip and the sample, which corresponds to the force at the maximum extension of the piezo and gives control of the stresses applied to the sample⁸⁵. Using cantilever oscillations with such high frequencies in liquid was only made possible by the development of specific soft cantilevers with a high resonant frequency and tip shapes that allows for instance imaging of microvilli on live epithelial cells⁸⁶. These modes are sometimes called multiparametric AFM modes since they allow to perform nanomechanical mapping on a large range of samples with speeds similar to those generally used for imaging⁸⁷. For instance, Eghiaian et al. showed that fast mechanical maps of living cells can be achieved with conventional AFM by combining PF-QNM mode with small AC40-TS cantilevers from Olympus⁸⁸. With such a combination, mechanical mapping of flat regions of cells was achieved with a scan rate of 16 seconds per frame by using a 128 pixel fast-scan axis and a 32 pixels slow-scan axis (pixel size of 15 nm for the highest-resolution images).

While these approaches are effective in reducing the time required to acquire images
or to perform mechanical measurements, they remain extremely time consuming for
users and do not really provide a viable solution to the low throughput problem.
Indeed, the user must always be involved at the end of each acquisition to move to the next area of interest and/or cells. Aware of the limits reached in the "race for speed", several teams have turned their attention to alternative approaches, acting on the way data is acquired, i.e., by focusing on parallelization of measurements or automation.

## 3.1. Parallelization of measurements

Classically, AFM works on the principle of a single lever scanning the surface of the sample, enabling sequential analysis of areas of interest. A number of studies have focused on the development of systems combining several levers in parallel, or multiple AFMs scanning different areas of one or more samples.

Since the late 90s, several teams have been developing such systems, with up to several hundred levers operating in parallel⁸⁹⁻⁹¹. As it was not possible to track the deflection of each lever using conventional optical detection based on a laser beam and a segmented photodiode, the groups turned to a piezoresistive strain gauge detection system. In addition to being expensive to manufacture, these systems are difficult to transpose to the study of biological samples. More recently, a group from Neuchâtel has proposed interferometric detection³. In this study, the authors succeeded in imaging fixed fibroblasts using an array of 35 levers. They also demonstrated the possibility of simultaneously imaging a set of force curves using 17 levers in parallel. Although particularly appealing and promising, this approach has not subsequently proven its effectiveness, notably due to significant limitations. The first is the impossibility of accurately determining the precise area of each cell being scanned (edge, nucleus), or even if the measurement is performed away from a cell, i.e., on the glass. One solution would be to impose fixed cell positions, corresponding to the layout of the AFM probes. Micropatterning (see sample preparation section) would then be an interesting solution. However, the filling rate of micropatterns is generally limited to about 40 % (depending on the technique). Moreover, it would be very challenging to precisely control the force applied to each individual cell. Another limitation of the cantilever array is the specificity of developed shapes that excludes the latest generation of levers and tips specifically designed to optimize cell studies (geometry to reduce noise and hydrodynamic effects, increase acquisition speed, etc.). Recently developed modes aforementioned, such as PF-QNM, are also excluded.

## 3.2. Automation

Automation of measurement acquisition is a field that has attracted a great deal of interest in recent years. AFM and scanning probe microscopy<sup>92,93</sup> automated solutions have already proven to be successful in fields of application far beyond biology, such as nanoscale defects identification on silicon wafers. However, the transposition of these techniques to biological samples imposes specific constraints which have considerably limited the development of automation.

Launched in 2009 by JPK, the ForceRobot remains the only bio-focused fully automated system commercially available. This device offers automatic alignment of detection system and calibration features, enabling it to acquire up to 10000 force curves per hour. Nevertheless, ForceRobot remains specifically dedicated to force spectroscopy, as it does not offer image acquisition of the samples nor extraction of nanomechanical properties maps.
While commercial systems have been offering automated AFM data acquisition for a number of years for very specific applications, no commercial system is currently dedicated to biological samples such as cells or tissues. As a result, a number of laboratories have been focusing on developing systems that enable a variable degree of automation, always with the aim of reducing human intervention and increasing throughput.

An automated AFM setups was developed in 2012 by Wang and collaborators⁹⁴ to automatically probe the mechanical properties of round Raji cells, which were fixed with glutaraldehyde prior to AFM measurements. An image-processing method, based on a circle detection algorithm, was used to detect all the cells into the field of view. The AFM was then engaged on the center of each circle and a single scan line was performed along each of the two horizontal axes. The limits of the cells are then detected from these two lines and assuming circular cell shapes, the center of each cell can be extrapolated. The AFM tip can then be moved to defined locations around the cell center for elasticity measurement. With this approach, the authors succeed in measuring mechanical properties of a cell in about 12 s (4 force curves). Although a real step forward compared to the traditional manual method, this approach is still closely dependent on the circular shape of the cells and can hardly be applied for other cell types and configurations (cell shape, cell confluence, etc.). Moreover, manual intervention is always required to switch from one optical field to the next or during sample exchange, which does not guarantee true automation.

The following year, Roy and collaborators⁹⁵ developed a semi-automated image-guided micro positioning system to align the AFM probe on human breast-tissue specimen. This system allows to track the tissue at coarse and fine magnifications and to perform AFM indentation measurements on specific regions of the tissue sample that have been previously identified as normal or cancerous by a pathologist. System validation is illustrated by the ability to characterize a total of 480 points in a region of approximately 80 mm by 150 mm in about 80 minutes; this seems a little faster than the time it would take a user using a manual positioning system (from 2 to 4 hours, depending on expertise). The proposed approach does not significantly increase throughput either. Indeed, human intervention is always required, both to determine the areas of interest on the samples and to make the transition from one section to another.

Attempts to develop a fully automated system was carried out in 2019 by Dujardin and collaborators¹. The system was based on a Bruker FastScan, whose stage was modified to be the support of a multi-sample plate (called Multi-Well). This Multi-Well plate has 12 wells, each of which can hold a different sample and be filled with liquid, enabling experiments to be carried out in air or in liquid. In addition, this system enables automatic detection of regions of interest, such as detecting and identifying individual cells within the sample. To this end, a large low-resolution survey scan (30 µm x 30 µm) is first performed. This survey scan is then analyzed automatically to detect the different cells using a height threshold. Finally, the system moves and acquires a higher resolution force map on each position, with a scan size predefined by the user. One of the major advantages of this system is the ability to analyze samples in different wells of the Multi-Well, allowing different experimental conditions to be compared. As a proof of concept, the authors succeeded in showing that the system was able to detect and image around 500 bacteria in just over 8.5 hours, i.e., around 60 seconds per bacterium. It is also worth mentioning that the device was designed to incorporate different features such as contamination detection and quality control for the tip, bubble detection and a tip cleaning procedure device as
well. Although this automated system has proved to be well suited for working with
bacteria, it highlighted the need to further develop another platform to work on
mammalian cells, with environmental control, state-of-the-art optical integration, and
a larger AFM scanning area.

Another automated AFM methodology was proposed the same year by Proa-Coronado and collaborators⁹⁶, with the particularity of combining both AFM automation and cell arrays. Cell immobilization was ensured by microwells of a microstructured polydimethylsiloxane (PDMS) stamp, making it possible to define the precise positions occupied by the cells in the sample. AFM automation was run by a Jython script and executed on the JPK NanoWizard bio-AFM platform (JPK Germany), enabling to perform mechanical measurements (force curves or force maps) on each microwell inside the AFM piezo range (100 µm x 100 µm). Once the AFM tip has scanned all the positions in this field, the motor stage automatically moves the tip to another area and then analyzes the microwell positions. Using *Candida albicans* cells, the authors report that they were able to measure, within 4 h, a population of 900 cells.

This approach is undeniably a major step forward in the development of a system
capable of increasing AFM throughput, enabling the analysis of large numbers of
cells without external intervention. If the immobilization device proves to be perfectly
suited to microorganisms⁹⁶, the use of micropatterned surfaces enable the device to
be transferred to mammalian cells. In a recent study⁹⁷, cell patterning and AFM
automation were combined to measure up to 956 cells. The researchers adapted the
Jython script from Proa et al. to accommodate the larger displacements needed for
mammalian cells. While the script leverages the stage motors, it is not without its
limitations, as the authors note the occasional need to manually reposition the tip on
the cells due to the motors' relative inaccuracy. However, as the AFM tip scans all
the positions, the results are highly dependent on the filling rate of each well/pattern,
which can be a real issue because of the occupancy rate. Although height-based
control is available during measurement, it can only rule out positions where there
are no cells, without considering other situations that may arise (multiple cells, cell
debris). Correlation with an optical image associated with each position, which can be
analyzed a posteriori, could be an improvement to fill this gap. Finally, this system
does not tackle the multi-sample issue and requires the user to manually change the
sample.

In recent years, the rapid development of artificial intelligence (AI) and deep learning
has led to another approach to AFM data acquisition. A number of projects have
focused on developing automated AFM control algorithms based on the detection of
deep learning objects using the popular YOLO neural network (see didactic box #2).
Work has been carried out to detect and locate molecules⁹⁸ or cells³⁹ in optical
images using YOLOv3. Rade and colleagues³⁹ established closed-loop scanner
trajectory control to measure multiple cell samples at high speed for automated
navigation. Combining these two improvements, they accelerate AFM probe
navigation to the desired cell shape by about 60 times compared to manual
experiments, demonstrating that the AI-based AFM operational framework
significantly increases measurement throughput. More recently, the work of Yang et
al.⁹⁹ presents AFM automation coupled with a deep learning model to perform force
spectroscopy measurements. The aim is to identify different cell types (HGC-27,
MCF-7, MGC-803, Raji and dead cells) by image recognition and to analyze them.
Their deep learning model is a modified YOLOX model that detects the positions of
the tip and all the cells (with their type and viability) in an optical image. From these
detected positions, the AFM tip can automatically move from cell to cell for analysis.

To conclude this chapter on data acquisition, we believe that hardware multiplexing is not the most promising way forward. The development of automatic routines, or in the future those enhanced by artificial intelligence, seems to us to be better suited for the automation of biological measurements. This approach has already provided some fine examples of recent achievements, and will no doubt continue to progress in the near future. A detailed and critical discussion on acquisition can be found in chapter 5.

4. Using Artificial Intelligence Tools on AFM Data

A few years ago, Kanilin examined the process of scientific discovery in SPM by tracing the flow of information from the tip-surface junction to a knowledge admitted by the scientific community. His work significantly advanced the integration of big data in the field of SPM, making them deeper and smarter¹⁰⁰. AFM can record data in the form of images or force curves. In both cases, the data must first be processed before it can be classified. It is on the basis of this observation that we have divided this 4th part into two subparts, themselves divided into two parts. In the first part, we discuss the use of AI tools for AFM images, both for processing raw images (1st subsection) and for classifying these processed images (2nd subsection). We then look at the force curves measured by AFM and, in the same way as before, divide them into processing the force curves (3rd subsection), then classifying these force curves (4th subsection)

4.1. AI for processing raw AFM images

Image quality plays a critical role in obtaining accurate and reliable data for analysis, interpretation, and classification. In this context, deep learning algorithms are widely used, particularly the convolutional neural network (CNN) shown in Fig. 3 and in the didactic box #2. CNNs have proven to outperform human observers, as they can detect a lot of small objects in a scene with greater accuracy and speed than humans.

<figure><img src="image_5.png" /></figure>

**Figure 3 – Classification pipeline based on various inputs, including images and FCs.** Fig. 3 was adapted with permission from License Number 5912531260926. Copyright 2023/ Junxi Wang, Mingyan Gao, Lixin Yang, Yuxi Huang, Jiahe Wang, Bowei Wang, Guicai Song, Zuobin Wang Elsevier Journal of Structural Biology¹⁰¹.
The most popular model is the YOLO algorithm (see didactic box #2) family. Zhang, Ren, et Hu in 2023¹⁰² have trained a pipeline with a YOLOv5 (v5 is for version 5) and a ResNet-34 in order to detect AFM imaging defects. YOLOv5's goal is to detect and locate the defect while ResNet-34 classifies the different types of defects. ResNet-34 is a residual neural network that comprises 34 convolutional layers with skip connections to mitigate the vanishing gradient problem. Skip connections act as shortcuts between two layers in the model, allowing for better optimization and accuracy. The integration of YOLOv5 and ResNet-34 provides an efficient and accurate solution for detecting and classifying defects on AFM imaging data, leading to improved image quality analysis and evaluation.

Reinforcement learning is another way for assessing data quality. In reinforcement learning, actions send to the agents rewards or penalties from the model based on their outcomes. It is a continuous learning process that improves with the number of actions. The more actions the model makes, the more the model learns. By combining reinforcement learning with deep learning a powerful technique is obtained called deep reinforcement learning. This technique was applied by Kull et al.¹⁰³ to control the probe and perform data quality control in their experiments. They separated the model in two parts: assessment and probe repair. The assessment is based on a CNN trained to check the quality of images generated by the probe, classifying them as good or bad images. If the image is classified as bad, the probe repair takes over and sends the image to a second CNN to determine the next appropriate action for the probe. The probe repair uses a Deep Q-Network (DQN, Q is for Quality) to evaluate the quality of probe actions and select the best one. This approach demonstrates how deep reinforcement learning can be applied effectively in AFM data quality control by combining it with CNNs and DQNs. While this model represents an essential component of AFM automation, it is only one step in a larger sequence that includes functions such as approaching the probe and acquiring images.

Once image quality has been evaluated, the next step is to extract the relevant information from images. However, extracting relevant information from images by manual techniques can be cumbersome and time consuming. Automation is a crucial method to address this challenge as it allows for fast, robust, and accurate extraction of information from the diverse types of images generated by an AFM experiment such as topography, force curve maps or even optical images. In particular, force curve maps, also called force volume maps (FV) produce a very large amount of data. Each data set contains 2D arrays of up to 256 x 256 force curves or more with faster scanners. From each force curve, a large number of parameters such as contact point, elasticity Modulus, rigidity, adhesion force or energy and deformation can be extracted producing for each parameter a 2D image from the 2D array. With such a large amount of data to analyze, automation becomes indispensable. The open-source software, FC_analysis¹⁰⁴, specializes on force curves (FC) and force volume (FV) maps. Its goal is to automatically analyze the input data to generate elasticity and deformability images as output. This software data analysis method is based on optimization problems with a lot of parameters that is closer to classical methods yet, FC_analysis provides a powerful tool for processing force curve maps, demonstrating the potential of automated techniques in the field of AFM data analysis.
Machine learning can be applied to AFM's images to determine the maturation status of cells as demonstrated in the paper of Lohrer et al.¹⁰⁵. The objective is to classify individual cell images into immature, mature, or supra-mature categories. Several steps are necessary to achieve this goal: first, data preprocessing needs to position the cell at the center of the image, converting it to grayscale using the White Top-Hat Transform (WTHT) algorithm. It splits the image of the cell in three regions: the cell body, the first ring and the second ring around the cell body. Secondly, for each cell region a feature extraction and selection are applied. These features are based on the roughness of the cellular membrane and are generated using the Local Binary Pattern (LBP) and the Histogram of Oriented Gradients (HOG) algorithms. LBP and HOG extract rough and smooth features among cells. LBP was initially used for extracting textures in an image and HOG has been proven to be effective for recognizing object shape and geometry. Those algorithms work at the same time on the image to extract features.

After extracting all the features, a selection step is required using a reduction dimension algorithm, called ReliefF. This algorithm selects the most relevant features from the initial set, reducing computational complexity and improving classification accuracy. Finally, they train a machine learning algorithm to classify the selected features. In this paper, the Support Vector Machine (SVM) was used (see didactic box #2). SVM uses hyperplanes to divide two classes represented by feature vectors; it's a binary classification. As a reminder, there are three classes. So, to correct this problem, they use ECOC (Error-Correcting Output Codes). The ECOC framework combines binary classifiers into a classifier capable of solving a multiclass problem. To ensure the ECOC classifier works, they do a cross-validation.

## 4.2. AI for the classification of processed AFM images

In the field of materials research, it is common practice to compare simulated AFM images with experimental results to better recognize molecule organization. ML algorithms trained on simulated data can then be used to classify real images and identify molecular organization and signatures on surfaces. Such algorithms are designed to classify features, spot patterns, predict results and make decisions. Currently the preferred method for AFM classification is supervised ML, however it requires upfront data preparation and processing that requires labeling the database before ML can be tested with unidentified samples. For example, Alldritt et al.¹⁰⁶ have developed an algorithm to predict the molecular structure of different conformations of 1S camphor on Cu (111). They created an image descriptor to replicate the molecular structure in 2D from the molecule's atomic positions. They then trained a convolutional neural network (CNN) to reproduce this image descriptor directly from a database of 134,000 small isolated organic compounds to provide simulated AFM images. They finally validated their algorithm using simulated images of molecules not included in the training data. Similarly, the team of Carracedo-Cosme et al.¹⁰⁷ succeeded in generating a free database from a selection of more than 685,000 organic molecules with a total of 165 million simulated AFM images, called QUAM-AFM. Currently, this makes it the largest classification reference for chemical compounds measured by AFM and an open-access database. The simulations can also be verified using AFM force curve measurements. The processed force curve provides information that can determine whether the material is soft or more rigid. This can be useful for finding out about the viscoelastic and adhesive properties of polymers¹⁰⁸. In this study, the authors developed an algorithm based on the enhanced Attard model (EAM), simulating different force curves under various conditions, such as microcantilever geometries and parameters of the microcantilever and diverse viscoelastic and adhesive properties of the materials. They generated a
database of simulated AFM experiments in Peak Force tapping mode over a range of input parameters to train and test the ML algorithm. They then employed this ML algorithm on AFM experimental data from a set of elastomer samples to determine the surface mechanical properties of their samples. ML tools are used to facilitate many types of prediction related to an object. This technology is used in many fields, including predicting the power conversion efficiency of organic photovoltaic cells from AFM images¹⁰⁹, the identification of molecular systems from experimental and theoretical AFM images¹¹⁰, the distinction of single, double or multiple break events¹¹¹ and the extraction of geometric features of DNA filamentous structures from AFM images¹¹². In addition, the field of biology is beginning to adopt ML-based classification for AFM data, which we will explore in more depth later in this chapter.

In biology, it is generally essential to carry out experiments on a large number of samples to ensure accuracy and reliability. To this purpose automation will play a major role as it will unlock the low number of cells usually analyzed by non-automated AFM and enable access to the large data sets required for accurate machine learning. Despite the cell number limitation, there are different ways of classifying AFM data from biological samples. One way to analyze these samples is to consider AFM topographic images and their surface specific features. For example, this was used to differentiate and classify cancerous cells from non-cancerous cells in the same organ. Several software processing strategies have been employed to extract various features from an AFM force map in some case using Python code¹¹³ and in other cases using Gwyddion (image processing software for scanning microscopes)¹¹⁴,¹¹⁵. However, in this way, feature extraction is not able to provide enough data for deep learning (DL), which is why AFM images are classified via ML instead¹¹⁶. In the study by Prasad et al.¹¹³, the authors classify a human colon cancer epithelial cell line with the same cell line that has been transfected with an RNAi targeting the CSK tumor suppressor protein making it more aggressive. They used the ringing AFM mode to obtain their images. This mode is similar to PeakForce mode, but is based on the dynamic response of the cantilever after detachment of the AFM probe from the sample surface and therefore provides additional information on the adhesion properties of the sample, such as restored adhesion and viscoelasticity. They extracted 10 surface features from these AFM images and classified them using an automatic gaussian process classifier. Similarly, in the studies by Yu et al.¹¹⁴ and Ito et al.¹¹⁵, the authors classify height AFM images. They extracted between 9 and 14 features from these images (including "Maximum peak height (Sp)", "Maximum pit depth (Sv)", "Maximum height (Sz)", "Projected area", "Surface", "Volume"). These features extracted from the AFM images are then classified using a support vector machine (SVM) algorithm. In this way, the authors can distinguish prostate cancer cells from non-cancerous cells¹¹⁴ or predict the cancerous origin of the host cell from its exosomes¹¹⁵. Other studies, conversely, have sought to classify using DL tools taking advantage of the different channels offered by AFM force map images (surface morphology, Young's modulus, adhesion force). These channels are combined to feed DL algorithms for cell type recognition¹⁰¹. The authors used the images of these 3 physical properties of the cells for convolutional neural network (CNN) analysis to differentiate normal liver cells from hepato-carcinomatous cells and gastric cancer cells from normal gastric cells. They were able to measure between 164 and 246 AFM images per cell line which they augmented using color changes, transformations, flips and noise additions to better populate the training base with between 656 and 984 images per cell line. It is
also possible to classify the force distance (FD) curves considered as an image. Minelli et al.¹¹⁷ were able to automatically classify FD curves of surgically removed brain tissue as normal or cancerous using a neural network. Subsequently, they decided to assign a whole brain tissue as either healthy or tumorous based on the analysis of 3 force curves¹¹⁸. Class membership was assigned on the basis of the majority of assignments. The need to apply a threshold from several FD curves measured on the same cell makes sense to classify that particular cell into one category or another. For example, Thomas-Chemin et al. decided that 4 force curves per cell were classified as cancerous out of 16 measured force curves, the cell was considered as cancerous. This idea can also be extended to the diagnosis of cancer in an individual. This was done in the study by Sokolov et al.¹¹⁹, in which the authors collect samples from over 60 patients with and without bladder cancer. This forms their database, which they then divide into a training database and a test database. They analyzed 5 urine cells per patient. Each cell is classified as cancerous or non-cancerous. The authors then decided to set a threshold of 2 out of 5 cells classified as cancerous in order to assign a cancerous diagnosis to the patient and to do an application of classification by ML in clinical practice. Another study¹²⁰ proposes to identify particular areas within the AFM images of height, adhesion and viscoelasticity that will allow better discrimination of highly or weakly aggressive colon cancer cell populations using a ML algorithm. Here again, the authors convert whole AFM images into feature values (e.g. roughness average, root mean square (RMS), surface skewness, surface kurtosis). They then classify these values as highly aggressive or less aggressive using regression and decision tree algorithms.

In the previous sections, the image quality and analysis were mentioned, but the force curves also need to be checked and analyzed.

### 4.3. AI for processing AFM force curves

As with images, it is also possible to sort force curves according to their quality. However, this type of data can have different file formats depending on the AFM used to measure the curves. One study¹²¹ looked at the problem of the language of AFM files depending on the AFM used. The authors therefore created an open source software called OpenFovea, capable of processing and analyzing force-volume files and displaying the results of Bruker, Digital Instruments, and Asylum Research files. However, this publication dates back to 2012 and since then there have been numerous updates to the AFM software and AFM data processing software. As a result, OpenFovea has become obsolete. With advances in AI and classification, it is becoming increasingly necessary to develop a common language, open to all and of reasonable size, to open these files. It could be interesting to create libraries of AFM data in HDF5 (hierarchical data format) so that each user can use the data to feed their ML algorithms. Jha et Agarwal¹²² use this type of HDF5 format for AFM images to make predictions of other AFM images as a function of different test parameters such as maximum applied load, loading rate or holding time, making it possible to characterize materials without performing experiments. Moreover, some papers have still tried to sort the force curves according to their quality. In the paper by Braunsmann et Schäffer¹²³, the authors selected 50 irregular force curves measured on a silicone elastomer AFM calibration grid, which they annotated as bad force curves. They also modeled 100 force curves with different Young's moduli according to the Sneddon model, which they annotated as good force curves. This formed their
training data set. Then, using this algorithm based on a neural network, they were able to reject all the bad force curves that did not follow a Sneddon-type behavior recorded on another material. With this same goal, a software package called nanite was developed by Müller et al.¹²⁴. In this article, the authors present a method that automates the analysis of FD curve data and its sorting using machine learning, enabling more data to be analyzed in a reproducible manner. They set up a machine learning approach to analyze FD curves that includes data import, tip-sample separation, baseline correction, contact point retrieval, model fitting and removal of FD curves affected by artifacts. The algorithm must be able to sort and report on the quality of the data. The training set is pre-processed by removing curves with a bad contact point or a bad indentation. The training set was tested by training fraction and test fraction 100 times on different regressors in order to choose the best one, and it turned out that Extra Trees (see didactic box #2) gave the best results. The algorithm then depends on various hyperparameters such as the area of spikes in the indentation part, the curvature at the contact point, the flatness of the positive-gradient residuals, the slope of the baseline, as well as the choice of regressor and the size of the training set. However, this algorithm still has weaknesses with biological samples, as the model adapts better to hard tissue than soft tissue.

To take this a step further, studies have been carried out into the automated analysis of force curves. In Chang et al. study¹²⁵, the authors developed an automated contact point detection algorithm for AFM data. This algorithm searches for a linear elastic region rather than the contact point directly and assumes the linearity of the selected data. In the end, this enables it to distinguish rigid force curves from softer ones. However, the authors suspect that the algorithm fails to select accurate contact points for materials of the order of 1 kPa or less or for materials with viscoelastic behavior. A subsequent study¹²⁶ used the potential of one-dimensional convolutional neural networks (CNNs) to locate these contact points, which also enabled the analysis of AFM force measurements to be automated. Nguyen et Liu study¹²⁷ proposes to directly select the appropriate contact model based on the properties of the force curves using a linear discriminant analysis (LDA) ML algorithm without knowing the shape of the indenter or the indented material. This method is therefore an effective way of reducing the uncertainty of AFM-based nanomechanical measurements and also reduces the time required to process the force curves.

## 4.4. AI for classifying AFM force curves

As with AFM images, some studies have considered the values of different features extracted from AFM force curves for classification purposes. It has been shown that the use of 4 cellular mechanical values (elastic modulus, adhesion, work of adhesion and membrane tension), makes it possible to classify 4 different grades of bladder cancer cells using a GA-BPNN (Genetic Algorithm coupled with a Back-Propagation Neural Network)¹²⁸. Using these 4 features together gives better classification results than using them individually. This study was carried out on 80 cells which constitute the training and test base, the training base being made up of between 50 and 90 % of the cells. The same logic can be used by adding additional features and/or to differentiate other cell types¹²⁹. Similarly, the authors combined 7 cellular properties (elastic modulus, adhesion, work of adhesion, membrane tension, length, width and height) as values to classify more than 60 normal gastric cells, gastric cancer cells, hepatocarcinomatous and mouse fibroblasts using a GA-BPNN algorithm. This
combination of mechanical properties has been dubbed the mechanome and provides a robust and effective biomarker of malignancy⁴. In this publication, the authors present a more accurate and higher quality method than cytomorphology for better differentiating 340 cancerous cells from benign cells. They set up an SVM algorithm to classify cell malignancy on the basis of data on the elastic modulus and prestress of living cells, which corresponds to the cell mechanome. To validate their theory, they then classified primary cells from cancer cells using cross-validation with an SVM algorithm based on either cell mechanome data or cell topography data. Only the mechanome score suggested a correlation between mechanome and malignancy. The properties of prestress and elastic modulus, measured by AFM and referred to as the mechanome, show a robust and powerful biomarker of malignancy. This concept of mechanome has been further developed in a recent study⁹⁷ where seven features from approach/retraction force curves were extracted and combined to form a more detailed mechanome. It should be noted, however, that the mechanome is the totality of a cell's mechanical properties, and so 2 or even 7 parameters can only be a small part of the mechanome. Nevertheless, in this latest study, the 7 mechanical parameters were analyzed and combined by a fuzzy logic algorithm called LAMDA¹³⁰⁻¹³³, which enabled the authors to classify normal cells and cancer cells with a success rate of 73%, despite a very high similarity rate, parameter by parameter, between the 2 conditions (79 to 100% common values). One study combined the two approaches by using both force map images and mechanical values (stiffness and adhesion) to classify RAW 264.7 macrophages. These macrophages were polarized into different phenotypes: pro-inflammatory (by LPS) and pro-healing (by IL-4), which were compared with the resting phenotype. Classification was based on three characteristics of the AFM force curves (adhesion, Young's modulus at the beginning of the force curve and Young's modulus at the end of the force curve) and the morpho value of the AFM topographic map, as well as two values representing the roundness of the macrophage based on the topographic images. Classification was performed using a deep neural network (DNN) TensorFlow algorithm¹³⁴. All these studies are based on supervised ML, which requires preparation of the training base. The problem with supervised classification is that all the training data has to be labeled manually, which is time-consuming. An alternative is semi-supervised or unsupervised learning. To our knowledge, only two studies have used unsupervised ML for classification of AFM data. Nascimento et al.¹³⁵ and Ilieva et al.¹³⁶ used an ML approach to unsupervised clustering of retract force curves from experimental AFM data. In Nascimento et al.¹³⁵, they sought to group the force curves into three types of cellulose-tip interactions: the highest adhesion forces, the lowest adhesion forces and the intermediate adhesion forces. In Ilieva et al.¹³⁶, the authors were able to distinguish groups of force curve traces corresponding to the deployment of four different proteins. However, few studies have used unsupervised ML, as the classes derived must be assigned meaningful interpretations.

Looking further ahead, it is possible to envision an AFM that makes decisions in real
time on the basis of recorded measurements. In the Huang, Li, et Li¹³⁷ article, the
authors developed an AI-AFM based on an SVM algorithm that controls the AFM.
They were able to correctly classify ferroelectric and electrochemical materials in real
time by probing only the critical points of the maps such as domain walls (DW) and
grain boundaries. As a result, they were able to correctly classify 7174 SPM maps
recorded over 10 years with a correct classification rate of 97.3 % for 475 maps with 180° DWs and 99.6 % for 6699 maps without 180° DWs.

The different types of algorithm models for classifying the different samples cited in
this chapter are summarized in Table 2.

To conclude on this chapter, using machine learning to classify data and interpret AFM results is neither a miracle solution nor a revolution. It is a tool that must be considered with its limitations and prerequisites. In particular, it is essential to have databases of sufficient size to be able to use the algorithms wisely, and for their use to bring added value. There's no point in using machine learning to classify data that is clearly different, and which the human operator can easily differentiate. On the other hand, as in the case of the article by Thomas- -Chemin *et al.*, when the data between 2 conditions are very close and the only way to classify them is to integrate a lot of data, then machine learning comes into its own. These considerations are developed in Chapter 5: "Perspectives and roadmap".
<table><caption>**Table 2 – Table summarizing the main characteristics of ML algorithms used in the literature to classify AFM data.** We present for each reference the different objects that were classified, the size and quality of the bases used to train the algorithms and their validation and success rate. (CNN: convolutional neural network, EAM: enhanced Attard model, LR: linear regression, NLMF + BE: non-local means filtering and background equalization, GPC: Gaussian process classifier, SVM: support vector machine, RF: random forest, ERF: extremely randomized forest, GBT: gradient boosting trees, GA-BPNN: genetic algorithm coupling a back-propagation neural network, MLP: multi-layer perceptron, DNN: deep neural network, UC: unsupervised clustering). For a didactic presentation of these models see didactic box #2: Overview of AI models: Types and Uses.</caption><thead><tr><th rowspan="2">Classified sample</th><th rowspan="2">Training base</th><th colspan="2">Algorithm</th><th rowspan="2">Validation</th><th rowspan="2">Success rate</th><th rowspan="2">Ref</th></tr><tr><th>ML</th><th>DL</th></tr></thead><tbody><tr><td>Images of small organic molecules<br/>- From organic chemistry</td><td>Simulated and experimental</td><td>/</td><td>CNN</td><td>Exp and simulated (ref.)</td><td>45-82 %</td><td>102, 105</td></tr><tr><td>- Configurations of 1S-camphor on Cu (111)</td><td>134 000 simulated images</td><td></td><td>CNN</td><td>Exp and simulated</td><td>/</td><td>106</td></tr><tr><td>Viscoelasticity and adhesion of three elastomer grades</td><td>Simulated Peak Force tapping data</td><td>EAM</td><td>/</td><td>Experimental data</td><td>72-100 %</td><td>108</td></tr><tr><td>Power conversion efficiency of polymer: non-fullerene molecular acceptor organic photovoltaics</td><td>890 experimental images</td><td>LR</td><td>/</td><td>Experimental data</td><td>75-76 %</td><td>109</td></tr><tr><td>Identification of single-, double-, multiple- and non-rupture events</td><td>Experimental force curves on 42 samples</td><td>/</td><td>CNN</td><td>Experimental data (ref.)</td><td>67 %</td><td>111</td></tr><tr><td>Assess DNA accessibility of nucleosomes</td><td>Experimental images on 254 objects</td><td>NLMF + BE</td><td>/</td><td>Experimental data</td><td>65 %</td><td>112</td></tr></tbody></table>
<table><tbody><tr><td>Classification of cancerous vs. non-cancerous cells<br/>- Liver and gastric cells</td><td>2 195<br/>experimental images</td><td>CNN</td><td>Exp data</td><td>85-89 %</td><td>101</td></tr><tr><td>- Prostate cells</td><td>Experimental images</td><td>SVM</td><td></td><td>83 %</td><td>114</td></tr><tr><td>- Bladder cells</td><td>Experimental images on 48 patients</td><td>GPC</td><td></td><td>97 %</td><td>119</td></tr><tr><td>- Gastric cells</td><td>3 dimensional parameters + 4 cellular mechanical properties on 1 184 samples</td><td>GA-BPNN</td><td></td><td>94 %</td><td>129</td></tr><tr><td>- Breast, cervical and airway cells</td><td>Prestress and elastic modulus of 49 cells</td><td>SVM</td><td></td><td>81-89 %</td><td>4</td></tr><tr><td>- Colon cancer cells with different degrees of neoplastic aggressiveness</td><td>Experimental ringing mode AFM images on 37 cells</td><td>RF, ERF, GBT</td><td></td><td>98 %</td><td>113</td></tr><tr><td>- Bladder cancer cells with different cancerization stages</td><td>4 cellular mechanical properties</td><td>GA-BPNN</td><td></td><td>94 %</td><td>128</td></tr><tr><td>- Identification of specific geometrical regions of colorectal cells with different degrees of aggressiveness</td><td>Experimental images</td><td>GPC</td><td></td><td>94 %</td><td>120</td></tr><tr><td>Classification of cancerous vs. non-cancerous tissues<br/>- Brain tissues</td><td>500<br/>experimental force curves</td><td>/</td><td>MLP</td><td>Experimental data</td><td>76-97 %</td></tr><tr><td>- Brain tissues</td><td>200<br/>experimental force curves</td><td>/</td><td>MLP</td><td>Experimental data</td><td>90-99 %</td></tr><tr><td>Cell type prediction<br/>- Host colorectal, fibroblast and pancreatic cancer cells</td><td>Experimental images on 1 519 particles</td><td>SVM</td><td>Experimental data</td><td>60-83 %</td><td>115</td></tr><tr><td>- Phenotype of macrophages</td><td>4 cellular mechanical properties + 2 pixel position parameters of 100 images</td><td>MLP</td><td>Experimental data</td><td>89-100 %</td><td>134</td></tr><tr><td>Clustering into types of interactions<br/>- Cellulose-tip</td><td>7 000<br/>experimental force curves</td><td>UC</td><td>/</td><td>Simulated data</td><td>/</td></tr><tr><td>- Protein deployment</td><td>20 experiments + 400 000 traces from force curves</td><td></td><td></td><td>Experimental data</td><td>135</td></tr></tbody></table>
<table><tr><td>Pattern recognition, feature identification in ferroelectric materials and electrochemical systems and classification via adaptive experimentation with additional probing at critical domain walls and grain boundaries</td><td>10 experimental and simulated images</td><td>SVM</td><td>/</td><td>Experimental data</td><td>97-100 %</td><td>¹³⁷</td></tr></table>

## 5. Review limitations

One limitation of this review is that, while it provides a comprehensive overview of the advancements in bio-AFM, particularly regarding its automation and the use of AI for data analysis, the scalability of these techniques remains a challenge. The low throughput of AFM data, which hampers its widespread adoption in clinical diagnostics, is addressed, but the solutions presented—such as automation and AI integration—are still in developmental stages and are not yet robust enough for real-world medical applications. Furthermore, the complexity of AFM equipment and the requirement for highly skilled operators continue to limit its accessibility in standard clinical settings. The study also primarily focuses on AFM's application to living cells, leaving a gap in the exploration of its full diagnostic potential across other biological materials like tissues or complex biological systems. Additionally, while the role of AI in enhancing data analysis is highlighted, the generalization of AI models across diverse biological conditions and patient populations remains an unresolved issue that requires further investigation. AI analysis of AFM data in combination with other clinical data is not covered, and would be of major interest, but to our knowledge the scientific literature has not yet addressed the subject. Thus, while promising, these approaches still face significant practical and technical barriers before AFM can become a mainstream diagnostic tool in biomedicine.

This review is not addressing areas beyond biology or medicine. This is a clear limitation of the review that focuses on biological samples. While atomic force microscopy (AFM) has proven to be a powerful tool for exploring biological phenomena at the molecular and cellular levels, its application to broader fields like materials science, environmental studies, and nanotechnology encounters similar hurdles.

## 6. Perspectives & Roadmap

We have emphasized in this review the critical role of the volume of data measured by bio-AFM to carry out statistically relevant experiments. Working on a large cell population prevents drawing conclusions that would be erroneous or contradictory because based on too little data. From a statistical point of view, we can evaluate the minimum number of cells to be measured in order to differentiate between two populations that have a mean Young's modulus, adhesion force, viscoelasticity modulus, etc. of a given difference, of a given standard deviation for a given p-value. Fig. 4 shows that the lower the p-value, the greater the number of samples to be
analyzed, rising from 264 with a p-value of 0.05 to 602 with a p-value of 0.01 for a
difference in the means of the two populations of 2 000 and a standard deviation of 5
000. We can also see that as the difference between the means of the two
populations decrease, the number of samples to be analyzed increases, from 16 with
a difference of 1 000 to 42 with a difference of 600 for a p-value of 0.04 and a
standard deviation of 500. Finally, we can also observe that as the standard deviation
increases, so does the number of samples to be analyzed, rising from 26 for a
standard deviation of 1 000 to 602 for a standard deviation of 5 000 with a p-value of
0.01 and a difference between the means of 2 000.

<figure><img src="image_6.png" /><figcaption>**Figure 4** – Graph showing the number of subjects required (represented by the color scale) to compare the observed means of 2 populations as a function of their standard deviations and the desired p-value.</figcaption></figure>

To sum up, with less than 20 cells per group, it is only possible to discriminate, with a
p-value of 0.01, between two groups with a small difference between them, of the
order of 100 Pa (for Young's modulus), and not very dispersed, or two groups with a
large difference between them, of the order of 2,000 Pa, and very dispersed (Fig. 4).
However, to differentiate statistically between two close and dispersed populations,
the number of cells measured increases considerably. A minimum of 954 cells is
needed to ensure the reliability of a difference of 50 Pa between 2 populations with a
standard deviation of 200 Pa and a p-value of 0.01. This is the strongest argument
why AFM automation must be developed to increase the number of measured cells
and consequently consolidate the robustness of the results.
From a machine learning point of view, determining the optimal number of cells to measure before testing the algorithm presents a significant challenge, as ML is an empirical field. In the paper of Alwosheel, van Cranenburgh, et Chorus¹³⁸, a lot of rules-of-thumb are listed such as the sample size that must be at least a factor 10 to 100 times the number of the features. However, the dataset size depends on the task complexity and the robustness wanted. A lack of data can easily converge toward an overfitting or underfitting situation. The data size is very important for finding the best model. "Overfitting" happens when a model learns too much from the training data and can generalize the problem, while "underfitting" is the inverse situation. Open data allows is necessary for people to train their models in a simpler/faster way. Data augmentation on a small dataset can also help but is not a good long-term solution.

<figure><img src="image_7.png" /><figcaption>**Figure 5 – AFM automation and classification roadmap.** Each color corresponds to an area of automation (blue: hardware automation and implementation, green: software automation and purple: data analysis and processing). For each item, a gauge shows the current progress.</figcaption></figure>

Considering data acquisition, some hardware tools for automation are already available on some commercial microscopes like self-alignment of the laser, self-calibration of the cantilever, and force feedback scanning modes for unattended scanning (Fig. 5). This must be further implemented on systems. But some improvement must be performed by AFM manufacturers. First and foremost, a dedicated multi-biosample holder with temperature, CO₂, and evaporation control for living cell experiments in physiological conditions. Another missing block is the optical image detection/recognition in the AFM software for AFM positioning. AFM companies typically rely on third-party optical products and tend to implement limited software performance. As a result, the user must bypass AFM scanning software with complicated tricks not available to a typical user. This can be improved by the integration of widely open-source algorithms from the optical microscopy field. Tip quality control is also missing in AFM control software while this is a fundamental part of a good self-scanning system. Once the tip has been detected as damaged or contaminated, it can be automatically changed to a fresh one (as such a microscope is already available on the market) or cleaned using an automated procedure.

On the scanning software part, there is a need for a universal programming language for standardizing scanning algorithms. This is clearly not the case in 2024 as AFM companies implement different scripting languages. The community is also aspiring
for an open-source, size-optimized, universal data file format with metadata. This
would greatly help the data analysis pipeline, no matter the microscope brand, and
therefore the reproducibility of experiments and the development of the field. Once
large experimental data are produced, they must be processed in a (semi)-automated
fashion. Al is clearly the way forward; in this regard more annotated and classified
open database must be available online for the development of good analysis
algorithms. This would greatly help the adoption of Al by non-experts of the bio-AFM
field, benefitting from developments of other scientific fields such as physics,
chemistry, biology and computing.

Once a completely automated system is available, scientific and medical applications
will be numerous, from accurate cancer diagnosis to drug discovery or fighting
against antibiotic resistance. To move beyond more automation, it is essential to
understand the paradigm that AFM represents: AFM should be considered more as
an extension of our sense of touch rather than a traditional microscopy technique.
With advancements in AI on the horizon, it is only natural to consider approaches for
mechanical sensing that involve robotics and artificial cognition. One interesting
implementation of this idea is proposed by Yang et al.⁹⁹: they introduce deep learning
image recognition-assisted AFM.

AFM robotics and cognition also mean manipulation. Using dedicated tools such as the fluidFM<sup>139</sup> to inject or capture material into individual cell or even to manipulate cells will bring prehension to mesoscale robotics. The combination of AI recognition, mechanical sensing, and manipulation tools at the mesoscale of tissues and cells is a conceptual game changer in understanding and engineering life at the mesoscale and in many ways a complete change of paradigm for AFM.

Content of the supporting information files

The supplementary data contains an Abacus showing the number of subjects required to compare two observed means depending on their standard deviations and the desired p-value, followed by the formulas used to calculate the number of samples to be analyzed; and the number of publications over time that include the words AFM, artificial intelligence, automation, machine learning, deep learning and cell. The reader can find this information available online.

Acknowledgements

This work was supported by grants from the Agence Nationale de la Recherche (ANR) AutoBioTip - ANR-20-CE42-0017. This work, bearing the reference EUR CARe N°ANR-18-EURE-0003, benefited from a government grant managed by the Agence Nationale de la Recherche under the Programme Investissements d'Avenir.
# Didactic Box #1: Artificial Intelligence – An overview

<figure><img src="image_8.png" /></figure>

## Artificial Intelligence (AI) versus Machine Learning (ML) versus Deep Learning (DL)

AI encompasses all methods and techniques used to imbue computers or machines with intelligence. ML is a subset of AI, focusing on algorithms that enable systems to learn from data without explicit programming. And within ML, DL stands out for its utilization of neural networks, which are structured to mimic the way the human brain processes information, allowing for more complex learning and decision-making capabilities. (Fig. 1).

## Machine Learning vs Deep Learning

In ML, feature extraction is typically performed before the training phase using manual methods or predefined algorithms. Conversely, in DL, feature extraction is integrated into the learning process, where neural networks autonomously learn relevant features directly from the raw data, eliminating the need for manual feature engineering

## The need for Big Data of excellent quality and open

Generally, traditional Machine Learning algorithms often require hundreds of samples to achieve good performance, whereas Deep Learning models typically require even larger datasets, often on the order of thousands of samples for each class, to generalize effectively. This necessity for large amounts of data has led to the concept of "Big Data," where the focus is on generating, collecting, and processing vast volumes of data. However, it's not just about quantity; data quality is equally important. Before feeding data into either ML or DL models, it's essential to perform tasks like labeling, cleaning, and preprocessing to ensure that the data is accurate, relevant, and suitable for training. These preprocessing steps are crucial for enhancing the performance and robustness of the models, regardless of the approach used. Data sharing plays a significant role in advancing research and fostering collaboration in the field of AI. By making datasets publicly available, researchers can address the issue of data scarcity and enable others to reproduce results, validate findings, and develop new methods and algorithms. Open-sourcing not only the data but also the algorithms and model parameters used in research promotes transparency and reproducibility.

Data augmentation is a powerful technique used to artificially increase the size of a dataset, particularly when the available dataset is small. This method involves applying a variety of transformations or modifications to existing data samples to generate new ones. In the context of image data, these transformations can include rotation, flipping, scaling, translation, cropping, adding noise, adjusting brightness or contrast, and applying various filters.

<figure><img src="image_9.png" /></figure>

The type of learning indeed depends on the nature of the data and the task at hand.

In supervised learning, the dataset consists of input-output pairs, where each input is associated with a corresponding output label. The goal is to learn a mapping or relationship between inputs and outputs, enabling the model to predict the correct output for new, unseen inputs.

Semi-supervised learning deals with datasets where only a portion of the data is labeled, while the majority remains unlabeled. The model leverages both labeled and unlabeled data to improve performance, often by learning a representation of the data that captures underlying structures and patterns.

Unsupervised learning involves learning from datasets where the data points are not labeled. The goal is to uncover hidden patterns, structures, or relationships within the data without explicit guidance. Common tasks include clustering, dimensionality reduction, and density estimation.

Reinforcement learning is a type of learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, with the objective of maximizing cumulative reward over time. Reinforcement learning is well-suited for sequential decision-making tasks and has applications in areas like robotics, gaming, and autonomous systems.

These different types of learning cater to a wide range of problem domains and allow for the development of diverse and sophisticated models tailored to specific requirements and challenges.

## AI techniques and their applications

In classification tasks, the goal is to predict the category or class label of an input data point. This is typically done by learning a decision boundary that separates different classes in the input space. Classification is used for image recognition, sentiment analysis, spam detection.

Regression tasks involve predicting a continuous value or quantity based on input features. The goal is to learn a mapping from input variables to output values that best fits the data. Regression is used in fields such as finance, healthcare, and economics for tasks like stock price prediction, disease prognosis, and sales forecasting.

Clustering tasks aim to partition a dataset into groups or clusters such that data points within the same cluster are more similar to each other than to those in other clusters. Clustering is an unsupervised learning task and is used for tasks like customer segmentation, image segmentation, and anomaly detection.

Beyond classification, regression, and clustering, AI techniques are applied to a variety of advanced tasks such as object detection, image segmentation, natural language processing, and recommendation systems. These tasks often involve more complex data structures and require specialized algorithms and techniques tailored to the specific problem domain.
# Didactic Box #2: Overview of AI Models: Types and Uses

**Support Vector Machines (SVM)** in 1964, Vladimir Vapnik created the Support Vector Machines. This model is the one of the most used in Machine Learning because it's simple, fast and easy to understand¹. SVMs are supervised learning models used primarily for classification. They work by finding the hyperplane that best separates data from different classes in a high-dimensional space. SVMs can also address regression tasks. Initially, SVM algorithms construct hyperplanes to separate classes effectively, favoring those with minimal classification errors. While SVMs inherently handle linear problems, they can address nonlinear challenges through kernel tricks, extending their applicability.

<figure><img src="image_10.png" /></figure>

**Decision trees, random forest, extra trees, gradient boosting tree...** Decision Trees are a non-parametric supervised machine learning algorithm that make decisions by formulating a series of questions about the characteristics of the data. A tree begins with a root node, which initially serves as a decision node and symbolizes the entire dataset. Subsequently, the root node splits into child nodes, branching based on different features, thus segmenting the data proportionally. These nodes, also known as decision nodes, reflect the distribution of data within each subset. As the tree progresses, further splits occur until reaching terminal nodes, or leaf nodes. These leaf nodes encapsulate all potential outcomes in the dataset. For instance, in classification tasks, each leaf node corresponds to a distinct class in the dataset. Random forests are sets of decision trees that combine the predictions of several trees to obtain more robust and accurate predictions. Extra Trees, or Extremely Randomized Trees, are similar to Random Forests but introduce additional randomness during the construction of each tree. Instead of selecting the best split at each node based on feature optimization, Extra Trees randomly choose splits, which can lead to faster training times and potentially better generalization. Gradient Boosting Trees is a powerful ensemble learning technique that builds an ensemble of weak learners, typically decision trees, in a sequential manner. Each new tree is trained to correct the errors made by the previous ones, with a focus on minimizing the loss function. Gradient Boosting Trees often achieve state-of-the-art performance on various machine learning tasks and are widely used in competitions and real-world applications.

<figure><img src="image_11.png" /></figure>

This image represents the schematic of a decision tree with 2 features (X1, X2) and 2 output classes (Success or Failure). The decision tree consists of 3 decision nodes and 4 leaves (outputs). For example, here, the classifier will answer the question "Can I win the game?" and our data are X1 = 20 and X2 = 1. So, 1st node, X1 > 10 = Yes. Therefore, 2nd node, X2 < 5 = Yes, the output of the decision tree will be Success.

**Multiple Layer Perceptron (MLP): Artificial Neuron networks (ANN), Convolutional Neuron networks (CNN), Recurrent neural networks (RNN)...** Artificial neural networks are models inspired by the functioning of the human brain. They are composed of several layers of interconnected neurons, and during training, they learn from examples by adjusting the weights of connections between neurons to minimize the difference between predicted and actual outputs. This process, known as backpropagation, involves iteratively updating the network's parameters using optimization algorithms like gradient descent. ANNs are used for a wide variety of tasks, including classification, regression, image and text recognition, among others. CNNs are a variant of MLPs, designed specifically for image processing. They are capable of learning visual features and patterns at different scales, making them highly effective for tasks such as image classification, object detection and image segmentation. CNNs are trained using large datasets through a process of backpropagation. In 2016, J. Redmon invented You Only Look Once (YOLO) which is used to accurately detect objects in a scene. A modified CNN divides the image into a grid and classifies each box of the grid into an object or another. RNNs are used to model data sequences, where each element of the sequence depends on the preceding elements. They are widely used in natural language processing, machine translation, text generation and other tasks involving sequential data.

Key components of ANNs include:

- **Input Layer**: The input layer receives raw data or features and passes them to the subsequent layers for processing.

- **Hidden Layers**: Hidden layers are intermediate layers between the input and output layers. They perform complex transformations on the input data through weighted connections and activation functions. Deep neural networks contain multiple hidden layers, allowing for the extraction of hierarchical representations of the input data.

- **Output Layer**: The output layer produces the final predictions or outputs based on the processed information from the hidden layers. The number of neurons in the output layer depends on the nature of the task, such as classification (multiple neurons representing different classes) or regression (single neuron for continuous output).

<figure><img src="image_12.png" /></figure>

These models represent only a small selection of the many architectures available in AI. The choice of model will depend on many factors, including the nature of your data, the complexity of the task and the computing resources available.
References

(1) Dujardin, A.; Wolf, P. D.; Lafont, F.; Dupres, V. Automated Multi-Sample Acquisition and Analysis Using Atomic Force Microscopy for Biomedical Applications. *PLOS ONE* **2019**, *14* (3), e0213853.
https://doi.org/10.1371/journal.pone.0213853.

(2) Formosa, C.; Pillet, F.; Schiavone, M.; Duval, R. E.; Ressier, L.; Dague, E. Generation of Living Cell Arrays for Atomic Force Microscopy Studies. *Nat Protoc* **2015**, *10* (1), 199–204.
https://doi.org/10.1038/nprot.2015.004.

(3) Favre, M.; Polesel-Maris, J.; Overstolz, T.; Niedermann, P.; Dasen, S.; Gruener, G.; Ischer, R.; Vettiger, P.; Liley, M.; Heinzelmann, H.; Meister, A. Parallel AFM Imaging and Force Spectroscopy Using Two-dimensional Probe Arrays for Applications in Cell Biology. *J of Molecular Recognition* **2011**, *24* (3), 446–452.
https://doi.org/10.1002/jmr.1119.

(4) Wang, H.; Zhang, H.; Da, B.; Lu, D.; Tamura, R.; Goto, K.; Watanabe, I.; Fujita, D.; Hanagata, N.; Kano, J.; Nakagawa, T.; Noguchi, M. Mechanomics Biomarker for Cancer Cells Unidentifiable through Morphology and Elastic Modulus. *Nano Lett.* **2021**, *21* (3), 1538–1545.
https://doi.org/10.1021/acs.nanolett.1c00003.

(5) Binnig, G.; Rohrer, H.; Gerber, Ch.; Weibel, E. Surface Studies by Scanning Tunneling Microscopy. *Phys. Rev. Lett.* **1982**, *49* (1), 57–61.
https://doi.org/10.1103/PhysRevLett.49.57.

(6) Binnig, G.; Quate, C. F.; Gerber, Ch. Atomic Force Microscope. *Phys. Rev. Lett.* **1986**, *56* (9), 930–933.
https://doi.org/10.1103/PhysRevLett.56.930.

(7) Smolyakov, G.; Thiebot, B.; Campillo, C.; Labdi, S.; Severac, C.; Pelta, J.; Dague, É. Elasticity, Adhesion, and Tether Extrusion on Breast Cancer Cells Provide a Signature of Their Invasive Potential. *ACS Appl. Mater. Interfaces* **2016**, *8* (41), 27426–27431.
https://doi.org/10.1021/acsami.6b07698.

(8) Dufrêne, Y. F.; Viljoen, A.; Mignolet, J.; Mathelié-Guinlet, M. AFM in Cellular and Molecular Microbiology. *Cellular Microbiology* **2021**, *23* (7), e13324.
https://doi.org/10.1111/cmi.13324.

(9) Alonso, J. L.; Goldmann, W. H. Feeling the Forces: Atomic Force Microscopy in Cell Biology. *Life Sciences* **2003**, *72* (23), 2553–2560.
https://doi.org/10.1016/S0024-3205(03)00165-6.

(10) Zlatanova, J.; Lindsay, S. M.; Leuba, S. H. Single Molecule Force Spectroscopy in Biology Using the Atomic Force Microscope. *Progress in Biophysics and Molecular Biology* **2000**, *74* (1), 37–61.
https://doi.org/10.1016/S0079-6107(00)00014-6.

(11) Gadegaard, N. Atomic Force Microscopy in Biology: Technology and Techniques. *Biotechnic & Histochemistry* **2006**, *81* (2–3), 87–97.
https://doi.org/10.1080/10520290600783143.

(12) Chang, K.-C.; Chiang, Y.-W.; Yang, C.-H.; Liou, J.-W. Atomic Force Microscopy in Biology and Biomedicine. *Tzu Chi Medical Journal* **2012**, *24* (4), 162–169.
https://doi.org/10.1016/j.tcmj.2012.08.002.

(13) Cohen, S. R.; Bitler, A. Use of AFM in Bio-Related Systems. *Current Opinion in Colloid & Interface Science* **2008**, *13* (5), 316–325.
https://doi.org/10.1016/j.cocis.2008.02.002.

(14) Cheong, L.-Z.; Zhao, W.; Song, S.; Shen, C. Lab on a Tip: Applications of Functional Atomic Force Microscopy for the Study of Electrical Properties in Biology. *Acta Biomaterialia* **2019**, *99*, 33–52.
https://doi.org/10.1016/j.actbio.2019.08.023.

(15) Hinterdorfer, P.; Baumgartner, W.; Gruber, H. J.; Schilcher, K.; Schindler, H. Detection and Localization of Individual Antibody-Antigen Recognition Events by Atomic Force Microscopy. *Proc Natl Acad Sci U S A* **1996**, *93* (8), 3477–3481.

(16) Rief, M.; Gautel, M.; Oesterhelt, F.; Fernandez, J. M.; Gaub, H. E. Reversible Unfolding of Individual Titin Immunoglobulin Domains by AFM. *Science* **1997**, *276* (5315), 1109–1112.
https://doi.org/10.1126/science.276.5315.1109.

(17) Engel, A.; Gaub, H. E.; Müller, D. J. Atomic Force Microscopy: A Forceful Way with Single Molecules. *Current Biology* **1999**, *9* (4), R133–R136.
https://doi.org/10.1016/S0960-9822(99)80081-5.
(18) Puech, P.-H.; Poole, K.; Knebel, D.; Muller, D. J. A New Technical Approach to Quantify Cell-Cell Adhesion Forces by AFM. *Ultramicroscopy* **2006**, *106* (8), 637-644.
https://doi.org/10.1016/j.ultramic.2005.08.003.

(19) Beaussart, A.; El-Kirat-Chatel, S.; Sullan, R. M. A.; Alsteens, D.; Herman, P.; Derclaye, S.; Dufrêne, Y. F. Quantifying the Forces Guiding Microbial Cell Adhesion Using Single-Cell Force Spectroscopy. *Nat Protoc* **2014**, *9* (5), 1049-1055. https://doi.org/10.1038/nprot.2014.066.

(20) Noy, A.; Vezenov, D. V.; Lieber, C. M. CHEMICAL FORCE MICROSCOPY. *Annu. Rev. Mater. Sci.* **1997**, *27* (1), 381-421. https://doi.org/10.1146/annurev.matsci.27.1.381.

(21) Dague, E.; Alsteens, D.; Latgé, J.-P.; Verbelen, C.; Raze, D.; Baulard, A. R.; Dufrêne, Y. F. Chemical Force Microscopy of Single Live Cells. *Nano Lett.* **2007**, *7* (10), 3026-3030.
https://doi.org/10.1021/nl071476k.

(22) Kodera, N.; Yamamoto, D.; Ishikawa, R.; Ando, T. Video Imaging of Walking Myosin V by High-Speed Atomic Force Microscopy. *Nature* **2010**, *468* (7320), 72-76.
https://doi.org/10.1038/nature09450.

(23) Fukuda, S.; Uchihashi, T.; Iino, R.; Okazaki, Y.; Yoshida, M.; Igarashi, K.; Ando, T. High-Speed Atomic Force Microscope Combined with Single-Molecule Fluorescence Microscope. *Review of Scientific Instruments* **2013**, *84* (7), 073706. https://doi.org/10.1063/1.4813280.

(24) Dufrêne, Y. F.; Martínez-Martín, D.; Medalsy, I.; Alsteens, D.; Müller, D. J. Multiparametric Imaging of Biological Systems by Force-Distance Curve-Based AFM. *Nat Meth* **2013**, *10* (9), 847-854. https://doi.org/10.1038/nmeth.2602.

(25) L. Chopinet ; C. Formosa ; M.P. Rols ; R.E. Duval ; E. Dague. Imaging Living Cells Surface and Quantifying Its Properties at High Resolution Using AFM in QI™ Mode. *Micron*. 2013.

(26) Meister, A.; Gabi, M.; Behr, P.; Studer, P.; Vörös, J.; Niedermann, P.; Bitterli, J.; Polesel-Maris, J.; Liley, M.; Heinzelmann, H.; Zambelli, T. FluidFM: Combining Atomic Force Microscopy and Nanofluidics in a Universal Liquid Delivery System for Single Cell Applications and Beyond. *Nano Lett.* **2009**, *9* (6), 2501-2507. https://doi.org/10.1021/nl901384x.

(27) Darling, E. M.; Carlo, D. D. High-Throughput Assessment of Cellular Mechanical Properties. *Annu Rev Biomed Eng* **2015**, *17*, 35-62. https://doi.org/10.1146/annurev-bioeng-071114-040545.

(28) Alsteens, D.; Tay, S.; Müller, D. J. Toward High-Throughput Biomechanical Phenotyping of Single Molecules. *Nat Methods* **2015**, *12* (1), 45-46. https://doi.org/10.1038/nmeth.3216.

(29) Cribb, J. A.; Osborne, L. D.; Beicker, K.; Psioda, M.; Chen, J.; O'Brien, E. T.; Taylor II, R. M.; Vicci, L.; Hsiao, J. P.-L.; Shao, C.; Falvo, M.; Ibrahim, J. G.; Wood, K. C.; Blobe, G. C.; Superfine, R. An Automated High-Throughput Array Microscope for Cancer Cell Mechanics. *Sci Rep* **2016**, *6* (1), 27371. https://doi.org/10.1038/srep27371.

(30) Sadeghian, H.; Hojjat, Y.; Soleimani, M. Interdigitated Electrode Design and Optimization for Dielectrophoresis Cell Separation Actuators. *Journal of Electrostatics* **2017**, *86*, 41-49.
https://doi.org/10.1016/j.elstat.2017.01.012.

(31) Dufrêne, Y. F.; Pelling, A. E. Force Nanoscopy of Cell Mechanics and Cell Adhesion. *Nanoscale* **2013**, *5* (10), 4094. https://doi.org/10.1039/c3nr00340j.

(32) Ando, T. High-Speed Atomic Force Microscopy Coming of Age. *Nanotechnology* **2012**, *23* (6), 062001. https://doi.org/10.1088/0957-4484/23/6/062001.

(33) Stavrakis, S.; Holzner, G.; Choo, J.; deMello, A. High-Throughput Microfluidic Imaging Flow Cytometry. *Current Opinion in Biotechnology* **2019**, *55*, 36-43.
https://doi.org/10.1016/j.copbio.2018.08.002.

(34) Wilder, K.; Singh, B.; Arnold, W. H. Novel In-Line Applications of Atomic Force Microscopy. *Solid State Technology* **1996**, *39* (5), 109-114.

(35) Fritz, M.; Radmacher, M.; Gaub, H. E. Granula Motion and Membrane Spreading during Activation of Human Platelets Imaged by Atomic Force Microscopy. *Biophys J* **1994**, *66* (5), 1328-1334.

(36) Chang, Y.; Cheng, T.-Y.; Shih, Y.-J.; Lee, K.-R.; Lai, J.-Y. Biofouling-Resistance Expanded Poly(Tetrafluoroethylene) Membrane with a Hydrogel-like Layer of Surface-Immobilized
Poly(Ethylene Glycol) Methacrylate for Human Plasma Protein Repulsions. *Journal of Membrane Science* **2008**, *323* (1), 77–84. https://doi.org/10.1016/j.memsci.2008.06.023.

(37) Ribeiro, S. J. M.; da Silva, J. C. P. Genetic Algorithms and Biological Images Restoration: Preliminary Report. In *Advances in Artificial Intelligence — IBERAMIA 2002*; Garijo, F. J., Riquelme, J. C., Toro, M., Eds.; Springer: Berlin, Heidelberg, 2002; pp 340–349.
https://doi.org/10.1007/3-540-36131-6_35.

(38) Sundstrom, A.; Cirrone, S.; Paxia, S.; Hsueh, C.; Kjolby, R.; Gimzewski, J. K.; Reed, J.; Mishra, B. Image Analysis and Length Estimation of Biomolecules Using AFM. *IEEE Trans Inf Technol Biomed* **2012**, *16* (6), 1200–1207. https://doi.org/10.1109/TITB.2012.2206819.

(39) Rade, J.; Zhang, J.; Sarkar, S.; Krishnamurthy, A.; Ren, J.; Sarkar, A. Deep Learning for Live Cell Shape Detection and Automated AFM Navigation. *Bioengineering* **2022**, *9* (10), 522.
https://doi.org/10.3390/bioengineering9100522.

(40) Foncy, J.; Estève, A.; Degache, A.; Colin, C.; Dollat, X.; Cau, J.-C.; Vieu, C.; Trévisiol, E.; Malaquin, L. Dynamic Inking of Large-Scale Stamps for Multiplexed Microcontact Printing and Fabrication of Cell Microarrays. *PLOS ONE* **2018**, *13* (8), e0202531.
https://doi.org/10.1371/journal.pone.0202531.

(41) Sadeghian, H.; Herfst, R.; Dekker, B.; Winters, J.; Bijnagte, T.; Rijnbeek, R. High-Throughput Atomic Force Microscopes Operating in Parallel. *Review of Scientific Instruments* **2017**, *88* (3), 033703. https://doi.org/10.1063/1.4978285.

(42) Martinez-Rivas, A.; González-Quijano, G.; Proa-Coronado, S.; Séverac, C.; Dague, E. Methods of Micropatterning and Manipulation of Cells for Biomedical Applications. *Micromachines* **2017**, *8* (12), 347. https://doi.org/10.3390/mi8120347.

(43) Ressier, L.; Viallet, B.; Beduer, A.; Fabre, D.; Fabie, L.; Palleau, E.; Dague, E. Combining Convective/Capillary Deposition and AFM Oxidation Lithography for Close-Packed Directed Assembly of Colloids. *Langmuir* **2008**, *24* (23), 13254–13257. https://doi.org/10.1021/la8028726.

(44) Cerf, A.; Cau, J.-C.; Vieu, C.; Dague, E. Nanomechanical Properties of Dead or Alive Single-Patterned Bacteria. *Langmuir* **2009**, *25* (10), 5731–5736. https://doi.org/10.1021/la9004642.

(45) Dague, E.; Jauvert, E.; Laplatine, L.; Viallet, B.; Thibault, C.; Ressier, L. Assembly of Live Micro-Organisms on Microstructured PDMS Stamps by Convective/Capillary Deposition for AFM Bio-Experiments. *Nanotechnology* **2011**, *22* (39), 395102. https://doi.org/10.1088/0957-4484/22/39/395102.

(46) Séverac, C.; Proa-Coronado, S.; Formosa-Dague, C.; Martinez-Rivas, A.; Dague, E. Automation of Bio-Atomic Force Microscope Measurements on Hundreds of C. Albicans Cells. *JoVE* **2021**, No. 170, 61315. https://doi.org/10.3791/61315.

(47) Tu, C.; Huang, B.; Zhou, J.; Liang, Y.; Tian, J.; Ji, L.; Liang, X.; Ye, X. A Microfluidic Chip for Cell Patterning Utilizing Paired Microwells and Protein Patterns. *Micromachines* **2016**, *8* (1), 1. https://doi.org/10.3390/mi8010001.

(48) Peric, O.; Hannebelle, M.; Adams, J. D.; Fantner, G. E. Microfluidic Bacterial Traps for Simultaneous Fluorescence and Atomic Force Microscopy. *Nano Res.* **2017**, *10* (11), 3896–3908.
https://doi.org/10.1007/s12274-017-1604-5.

(49) Foncy, J.; Estève, A.; Degache, A.; Colin, C.; Cau, J. C.; Malaquin, L.; Vieu, C.; Trévisiol, E. Fabrication of Biomolecule Microarrays for Cell Immobilization Using Automated Microcontact Printing. In *Cell-Based Microarrays*; Ertl, P., Rothbauer, M., Eds.; Methods in Molecular Biology; Springer New York: New York, NY, 2018; Vol. 1771, pp 83–95. https://doi.org/10.1007/978-1-4939-7792-5_7.

(50) Théry, M.; Pépin, A.; Dressaire, E.; Chen, Y.; Bornens, M. Cell Distribution of Stress Fibres in Response to the Geometry of the Adhesive Environment. *Cell Motil. Cytoskeleton* **2006**, *63* (6), 341–355. https://doi.org/10.1002/cm.20126.

(51) Rigato, A.; Rico, F.; Eghiaian, F.; Piel, M.; Scheuring, S. Atomic Force Microscopy Mechanical Mapping of Micropatterned Cells Shows Adhesion Geometry-Dependent Mechanical Response
on Local and Global Scales. ACS Nano **2015**, *9* (6), 5846–5856.
https://doi.org/10.1021/acsnano.5b00430.

(52) Ndlovu, E.; Malpartida, L.; Sultana, T.; Dahms, T. E. S.; Dague, E. Host Cell Geometry and Cytoskeletal Organization Governs Candida -Host Cell Interactions at the Nanoscale. ACS Appl. Mater. Interfaces **2023**, *15* (44), 50789–50798. https://doi.org/10.1021/acsmami.3c09870.

(53) Lagraulet, A.; Foncy, J.; Berteloite, B.; Esteve, A.; Blatche, M.-C.; Malaquin, L.; Vieu, C. InnoStamp 40TM and InnoScan 1100ALTM: A Complete Automated Platform for Microstructured Cell Arrays. Nat Methods **2015**, *12* (9), i–iii. https://doi.org/10.1038/nmeth.f383.

(54) Fink, J.; Théry, M.; Azioune, A.; Dupont, R.; Chatelain, F.; Bornens, M.; Piel, M. Comparative Study and Improvement of Current Cell Micro-Patterning Techniques. Lab Chip **2007**, *7* (6), 672–680.
https://doi.org/10.1039/B618545B.

(55) Pérez-Domínguez, S.; Werkmeister, E.; Marini, M. L.; Dupres, V.; Janel, S.; Lafont, F.; Radmacher, M. Rheological Comparison between Control and Dupuytren Fibroblasts When Plated in Circular Micropatterns Using Atomic Force Microscopy. Front. Phys. **2022**, *10*, 1052203.
https://doi.org/w.

(56) Sri-Ranjan, K.; Sanchez-Alonso, J. L.; Swiatlowska, P.; Rothery, S.; Novak, P.; Gerlach, S.; Koeninger, D.; Hoffmann, B.; Merkel, R.; Stevens, M. M.; Sun, S. X.; Gorelik, J.; Braga, V. M. M. Intrinsic Cell Rheology Drives Junction Maturation. Nat Commun **2022**, *13* (1), 4832.
https://doi.org/10.1038/s41467-022-32102-9.

(57) Meister, A.; Polesel-Maris, J.; Niedermann, P.; Przybylska, J.; Studer, P.; Gabi, M.; Behr, P.; Zambelli, T.; Liley, M.; Vörös, J.; Heinzelmann, H. Nanoscale Dispensing in Liquid Environment of Streptavidin on a Biotin-Functionalized Surface Using Hollow Atomic Force Microscopy Probes. *Microelectronic Engineering* **2009**, *86* (4–6), 1481–1484.
https://doi.org/10.1016/j.mee.2008.10.025.

(58) Berganza, E.; Hirtz, M. Direct-Write Patterning of Biomimetic Lipid Membranes In Situ with FluidFM. ACS Appl. Mater. Interfaces **2021**, *13* (43), 50774–50784.
https://doi.org/10.1021/acsmami.1c15166.

(59) Helfricht, N.; Mark, A.; Behr, M.; Bernet, A.; Schmidt, H.; Papastavrou, G. Writing with Fluid: Structuring Hydrogels with Micrometer Precision by AFM in Combination with Nanofluidics. Small **2017**, *13* (31), 1700962. https://doi.org/10.1002/smll.201700962.

(60) Yu, X.; He, R.; Li, S.; Cai, B.; Zhao, L.; Liao, L.; Liu, W.; Zeng, Q.; Wang, H.; Guo, S.; Zhao, X. Magneto-Controllable Capture and Release of Cancer Cells by Using a Micropillar Device Decorated with Graphite Oxide-Coated Magnetic Nanoparticles. Small **2013**, *9* (22), 3895–3901.
https://doi.org/10.1002/smll.201300169.

(61) Vikrant, K. S.; Hithiksha, K.; Jayanth, G. R. An Automated AFM Tip-Replacement System for In Situ Tip-Replacement. *IEEE/ASME Trans. Mechatron. 2021*, *26* (2), 798–806.
https://doi.org/10.1109/TMECH.2020.3001200.

(62) Kumar, R. T. R.; Hassan, S. U.; Sukas, O. S.; Eichhorn, V.; Krohs, F.; Fatikow, S.; Boggild, P. Nanobits: Customizable Scanning Probe Tips. Nanotechnology **2009**, *20* (39), 395703.
https://doi.org/10.1088/0957-4484/20/39/395703.

(63) Sader, J. E.; Chon, J. W. M.; Mulvaney, P. Calibration of Rectangular Atomic Force Microscope Cantilevers. Review of Scientific Instruments **1999**, *70* (10), 3967–3969.
https://doi.org/10.1063/1.1150021.

(64) Sader, J. E.; Sanelli, J. A.; Adamson, B. D.; Monty, J. P.; Wei, X.; Crawford, S. A.; Friend, J. R.; Marusic, I.; Mulvaney, P.; Bieske, E. J. Spring Constant Calibration of Atomic Force Microscope Cantilevers of Arbitrary Shape. Review of Scientific Instruments **2012**, *83* (10), 103705.
https://doi.org/10.1063/1.4757398.

(65) Sader, J. E.; Borgani, R.; Gibson, C. T.; Haviland, D. B.; Higgins, M. J.; Kilpatrick, J. I.; Lu, J.; Mulvaney, P.; Shearer, C. J.; Slattery, A. D.; Thorén, P.-A.; Tran, J.; Zhang, H.; Zhang, H.; Zheng, T. A Virtual Instrument to Standardise the Calibration of Atomic Force Microscope Cantilevers. Review of Scientific Instruments **2016**, *87* (9), 093711. https://doi.org/10.1063/1.4962866.
(66) Sumbul, F.; Hassanpour, N.; Rodriguez-Ramos, J.; Rico, F. One-Step Calibration of AFM in Liquid. *Frontiers in Physics* **2020**, 8. https://doi.org/10.3389/fphy.2020.00301.

(67) Schillers, H.; Rianna, C.; Schäpe, J.; Luque, T.; Doschke, H.; Wälte, M.; Uriarte, J. J.; Campillo, N.; Michanetzis, G. P. A.; Bobrowska, J.; Dumitru, A.; Herruzo, E. T.; Bovio, S.; Parot, P.; Galluzzi, M.; Podestà, A.; Puricelli, L.; Scheuring, S.; Missirlis, Y.; Garcia, R.; Odorico, M.; Teulon, J.-M.; Lafont, F.; Lekka, M.; Rico, F.; Rigato, A.; Pellequer, J.-L.; Oberleithner, H.; Navajas, D.; Radmacher, M. Standardized Nanomechanical Atomic Force Microscopy Procedure (SNAP) for Measuring Soft and Biological Samples. *Scientific Reports* **2017**, 7 (1), 5117. https://doi.org/10.1038/s41598-017-05383-0.

(68) Ohler, B. Cantilever Spring Constant Calibration Using Laser Doppler Vibrometry. *Review of Scientific Instruments* **2007**, 78 (6), 063701. https://doi.org/10.1063/1.2743272.

(69) Langlois, E. D.; Shaw, G. A.; Kramar, J. A.; Pratt, J. R.; Hurley, D. C. Spring Constant Calibration of Atomic Force Microscopy Cantilevers with a Piezosensor Transfer Standard. *Review of Scientific Instruments* **2007**, 78 (9), 093705. https://doi.org/10.1063/1.2785413.

(70) Palacio, M. L. B.; Bhushan, B. Normal and Lateral Force Calibration Techniques for AFM Cantilevers. *Critical Reviews in Solid State and Materials Sciences* **2010**, 35 (2), 73–104. https://doi.org/10.1080/10408430903546691.

(71) Strahlendorff, T.; Dai, G.; Bergmann, D.; Tutsch, R. Tip Wear and Tip Breakage in High-Speed Atomic Force Microscopes. *Ultramicroscopy* **2019**, 201, 28–37. https://doi.org/10.1016/j.ultramic.2019.03.013.

(72) Kulkarni, S. G.; Pérez-Domínguez, S.; Radmacher, M. Influence of Cantilever Tip Geometry and Contact Model on AFM Elasticity Measurement of Cells. *J of Molecular Recognition* **2023**, 36 (7), e3018. https://doi.org/10.1002/jmr.3018.

(73) Sirghi, L.; Kylián, O.; Gilliland, D.; Ceccone, G.; Rossi, F. Cleaning and Hydrophilization of Atomic Force Microscopy Silicon Probes. *J. Phys. Chem. B* **2006**, 110 (51), 25975–25981. https://doi.org/10.1021/jp063327g.

(74) Alldritt, B. W. Characterization and Reconstruction of Single Molecules Utilizing Atomic Force Microscopy and Machine Learning; Aalto University, 2021.

(75) Dörig, P.; Ossola, D.; Truong, A. M.; Graf, M.; Stauffer, F.; Vörös, J.; Zambelli, T. Exchangeable Colloidal AFM Probes for the Quantification of Irreversible and Long-Term Interactions. *Biophysical Journal* **2013**, 105 (2), 463–472. https://doi.org/10.1016/j.bpj.2013.06.002.

(76) Morozov, I. A.; Izyumov, R. I. Challenges of Reliable AFM -tip Shape Reconstruction and Approximation. *Microscopy Res & Technique* **2024**, 87 (1), 105–113. https://doi.org/10.1002/jemt.24415.

(77) Thundat, T. Calibration of Atomic Force Microscope Tips Using Biomolecules. Scanning Microscopy **1992**, 6 (4), 903-910

(78) Barker, D. S.; Blowey, P. J.; Brown, T.; Sweetman, A. Automated Scanning Probe Tip State Classification without Machine Learning. *ACS Nano* **2024**, 18 (3), 2384–2394. https://doi.org/10.1021/acsnano.3c10597.

(79) Jacobs, T. D. B.; Wabiszewski, G. E.; Goodman, A. J.; Carpick, R. W. Characterizing Nanoscale Scanning Probes Using Electron Microscopy: A Novel Fixture and a Practical Guide. *Review of Scientific Instruments* **2016**, 87 (1), 013703. https://doi.org/10.1063/1.4937810.

(80) Ando, T.; Kodera, N.; Takai, E.; Maruyama, D.; Saito, K.; Toda, A. A High-Speed Atomic Force Microscope for Studying Biological Macromolecules. *Proceedings of the National Academy of Sciences* **2001**, 98 (22), 12468–12472. https://doi.org/10.1073/pnas.211400898.

(81) Ando, T.; Uchihashi, T.; Kodera, N. High-Speed AFM and Applications to Biomolecular Systems. *Annu. Rev. Biophys.* **2013**, 42 (1), 393–414. https://doi.org/10.1146/annurev-biophys-083012-130324.

(82) Uchihashi, T.; Watanabe, H.; Fukuda, S.; Shibata, M.; Ando, T. Functional Extension of High-Speed AFM for Wider Biological Applications. *Ultramicroscopy* **2016**, 160, 182–196. https://doi.org/10.1016/j.ultramic.2015.10.017.
(83) Fantner, G. E.; Barbero, R. J.; Gray, D. S.; Belcher, A. M. Kinetics of Antimicrobial Peptide Activity Measured on Individual Bacterial Cells Using High Speed AFM. *Nat Nanotechnol* **2010**, *5* (4), 280–285. https://doi.org/10.1038/nnano.2010.29.

(84) Shibata, M.; Watanabe, H.; Uchihashi, T.; Ando, T.; Yasuda, R. High-Speed Atomic Force Microscopy Imaging of Live Mammalian Cells. *Biophys Physicobiol* **2017**, *14*, 127–135. https://doi.org/10.2142/biophysico.14.0_127.

(85) Pittenger, B.; Erina, N.; su, C. Quantitative Mechanical Property Mapping at the Nanoscale with PeakForce QNM; Bruker Corporation 2010; Vol. 128. https://doi.org/10.13140/RG.2.1.4463.8246.

(86) Schillers, H.; Medalsy, I.; Hu, S.; Slade, A. L.; Shaw, J. E. PeakForce Tapping Resolves Individual Microvilli on Living Cells. *J Mol Recognit* **2016**, *29* (2), 95–101. https://doi.org/10.1002/jmr.2510.

(87) Smolyakov, G.; Cauquil, M.; Severac, C.; Lachaize, V.; Guilbeau-Frugier, C.; Sénard, J.-M.; Galés, C.; Dague, E. Biophysical Properties of Cardiomyocyte Surface Explored by Multiparametric AFM. *Journal of Structural Biology* **2017**, *198* (1), 28–37. https://doi.org/10.1016/j.jsb.2017.03.001.

(88) Eghiaian, F.; Rigato, A.; Scheuring, S. Structural, Mechanical, and Dynamical Variability of the Actin Cortex in Living Cells. *Biophys J* **2015**, *108* (6), 1330–1340. https://doi.org/10.1016/j.bpj.2015.01.016.

(89) Minne, S. C.; Yaralioglu, G.; Manalis, S. R.; Adams, J. D.; Zesch, J.; Atalar, A.; Quate, C. F. Automated Parallel High-Speed Atomic Force Microscopy. *Applied Physics Letters* **1998**, *72* (18), 2340–2342. https://doi.org/10.1063/1.121353.

(90) Vettiger, P.; Gross, G.; Despont, M.; Drechsler, U.; During, U.; Haberle, W.; King, W. P.; Lutwyche, M. I.; Rothuizen, H.; Stutz, R.; Widmer, R.; Binnig, G. K. The "Millipede"-More than One Thousand Tips for Parallel and Dense AFM Data Storage. In *2000 Asia-Pacific Magnetic Recording Conference. Digests of APMRC2000 on Mechanical and Manufacturing Aspects of HDD (Cat. No.00EX395)*; IEEE: Tokyo, Japan, 2000; p MC1/1-MC1/2. https://doi.org/10.1109/APMRC.2000.898915.

(91) Kawakatsu, H.; Saya, D.; Kato, A.; Fukushima, K.; Toshiyoshi, H.; Fujita, H. Millions of Cantilevers for Atomic Force Microscopy. *Review of Scientific Instruments* **2002**, *73* (3), 1188–1192. https://doi.org/10.1063/1.1448137.

(92) Liu, R. (Yu); Pratiush, U.; Bemis, J.; Proksch, R.; Kalinin, S. V. Automating Experiments with Scanning Probe Microscopy. *Microscopy and Microanalysis* **2024**, *30* (Supplement_1), ozae044.1010. https://doi.org/10.1093/mam/ozae044.1010.

(93) Ziatdinov, M. A.; Roccapriore, K.; LIU, Y.; Kelley, K. P.; Vasudevan, R. K.; Hinkle, J. D.; Kalinin, S. V. Science-Driven Automated Experiments. US11982684B1, May 14, 2024. https://patents.google.com/patent/US11982684B1/en (accessed 2024-09-06).

(94) Wang, Z.; Liu, L.; Wang, Y.; Xi, N.; Dong, Z.; Li, M.; Yuan, S. A Fully Automated System for Measuring Cellular Mechanical Properties. *J Lab Autom.* **2012**, *17* (6), 443–448. https://doi.org/10.1177/2211068212460236.

(95) Roy, R.; Chen, W.; Cong, L.; Goodell, L. A.; Foran, D. J.; Desai, J. P. A Semi-Automated Positioning System for Contact-Mode Atomic Force Microscopy (AFM). *IEEE Trans. Automat. Sci. Eng.* **2013**, *10* (2), 462–465. https://doi.org/10.1109/TASE.2012.2226154.

(96) Proa-Coronado, S.; Séverac, C.; Martinez-Rivas, A.; Dague, E. Beyond the Paradigm of Nanomechanical Measurements on Cells Using AFM: An Automated Methodology to Rapidly Analyse Thousands of Cells. *Nanoscale Horiz.* **2019**, *5* (1), 131–138. https://doi.org/10.1039/C9NH00438F.

(97) Thomas - Chemin, O.; Séverac, C.; Moumen, A.; Martinez-Rivas, A.; Vieu, C.; Le Lann, M.-V.; Trevisiol, E.; Dague, E. Automated Bio-AFM Generation of Large Mechanome Data Set and Their Analysis by Machine Learning to Classify Cancerous Cell Lines. *ACS Appl. Mater. Interfaces* **2024**. https://doi.org/10.1021/acsami.4c09218.
(98) Sotres, J.; Boyd, H.; Gonzalez-Martinez, J. F. Enabling Autonomous Scanning Probe Microscopy Imaging of Single Molecules with Deep Learning. *Nanoscale* **2021**, *13* (20), 9193–9203.
https://doi.org/10.1039/D1NR01109J.

(99) Yang, X.; Yang, Y.; Zhang, Z.; Li, M. Deep Learning Image Recognition-Assisted Atomic Force Microscopy for Single-Cell Efficient Mechanics in Co-Culture Environments. *Langmuir* **2024**, *40* (1), 837–852. https://doi.org/10.1021/acs.langmuir.3c03046.

(100) Kalinin, S. V.; Strelcov, E.; Belianinov, A.; Somnath, S.; Vasudevan, R. K.; Lingerfelt, E. J.; Archibald, R. K.; Chen, C.; Proksch, R.; Laanait, N.; Jesse, S. Big, Deep, and Smart Data in Scanning Probe Microscopy. *ACS Nano* **2016**, *10* (10), 9068–9086.
https://doi.org/10.1021/acsnano.6b04212.

(101) Wang, J.; Gao, M.; Yang, L.; Huang, Y.; Wang, J.; Wang, B.; Song, G.; Wang, Z. Cell Recognition Based on Atomic Force Microscopy and Modified Residual Neural Network. *Journal of Structural Biology* **2023**, *215* (3), 107991. https://doi.org/10.1016/j.jsb.2023.107991.

(102) Zhang, J.; Ren, J.; Hu, S. AFM Imaging Defect Detection and Classification with Artificial Intelligence and Deep Learning. In *2023 IEEE 23rd International Conference on Nanotechnology (NANO)*; 2023; pp 447–452. https://doi.org/10.1109/NANO58406.2023.10231258.

(103) Krull, A.; Hirsch, P.; Rother, C.; Schiffrin, A.; Krull, C. Artificial-Intelligence-Driven Scanning Probe Microscopy. *Commun Phys* **2020**, *3* (1), 1–8. https://doi.org/10.1038/s42005-020-0317-3.

(104) Dinarelli, S.; Girasole, M.; Longo, G. FC_analysis: A Tool for Investigating Atomic Force Microscopy Maps of Force Curves. *BMC Bioinformatics* **2018**, *19* (1), 258.
https://doi.org/10.1186/s12859-018-2265-4.

(105) Lohrer, M. F.; Liu, Y.; Hanna, D. M.; Wang, K.-H.; Liu, F.-T.; Laurence, T. A.; Liu, G. Determination of the Maturation Status of Dendritic Cells by Applying Pattern Recognition to High-Resolution Images. *J. Phys. Chem. B* **2020**, *124* (39), 8540–8548.
https://doi.org/10.1021/acs.jpcb.0c06437.

(106) Alldritt, B.; Hapala, P.; Oinonen, N.; Urtev, F.; Krejci, O.; Federici Canova, F.; Kannala, J.; Schulz, F.; Liljeroth, P.; Foster, A. S. Automated Structure Discovery in Atomic Force Microscopy. *Science Advances* **2020**, *6* (9), eaay6913. https://doi.org/10.1126/sciadv.aay6913.

(107) Carracedo-Cosme, J.; Romero-Muñiz, C.; Pou, P.; Pérez, R. QUAM-AFM: A Free Database for Molecular Identification by Atomic Force Microscopy. *J. Chem. Inf. Model.* **2022**, *62* (5), 1214–1223. https://doi.org/10.1021/acs.jcim.1c01323.

(108) Rajabifar, B.; Meyers, G. F.; Wagner, R.; Raman, A. Machine Learning Approach to Characterize the Adhesive and Mechanical Properties of Soft Polymers Using PeakForce Tapping AFM. *Macromolecules* **2022**, *55* (19), 8731–8740.
https://doi.org/10.1021/acs.macromol.2c00147.

(109) Kobayashi, Y.; Miyake, Y.; Ishiwari, F.; Ishiwata, S.; Saeki, A. Machine Learning of Atomic Force Microscopy Images of Organic Solar Cells. *RSC Advances* **2023**, *13* (22), 15107–15113.
https://doi.org/10.1039/D3RA02492J.

(110) Carracedo-Cosme, J.; Romero-Muñiz, C.; Pérez, R. A Deep Learning Approach for Molecular Classification Based on AFM Images. *Nanomaterials* **2021**, *11* (7), 1658.
https://doi.org/10.3390/nano11071658.

(111) Waite, J. R.; Tan, S. Y.; Saha, H.; Sarkar, S.; Sarkar, A. Few-Shot Deep Learning for AFM Force Curve Characterization of Single-Molecule Interactions. *Patterns* **2023**, *4* (1), 100672.
https://doi.org/10.1016/j.patter.2022.100672.

(112) Würtz, M.; Aumiller, D.; Gundelwein, L.; Jung, P.; Schütz, C.; Lehmann, K.; Tóth, K.; Rohr, K. DNA Accessibility of Chromatosomes Quantified by Automated Image Analysis of AFM Data. *Sci Rep* **2019**, *9* (1), 12788. https://doi.org/10.1038/s41598-019-49163-4.

(113) Prasad, S.; Rankine, A.; Prasad, T.; Song, P.; Dokukin, M. E.; Makarova, N.; Backman, V.; Sokolov, I. Atomic Force Microscopy Detects the Difference in Cancer Cells of Different Neoplastic Aggressiveness via Machine Learning. *Advanced NanoBiomed Research* **2021**, *1* (8), 2000116. https://doi.org/10.1002/anbr.202000116.
(114) Yu, J.; Gao, H.; Si, X.; Yang, H.; Wang, Y. SVM-Based Classification on AFM Images of Prostate Cancer Cells. In *SPIE-CLP Conference on Advanced Photonics 2022*; SPIE, 2023; Vol. 12601, pp 23–28. https://doi.org/10.1117/12.2667179.

(115) Ito, K.; Ogawa, Y.; Yokota, K.; Matsumura, S.; Minamisawa, T.; Suga, K.; Shiba, K.; Kimura, Y.; Hirano-Iwata, A.; Takamura, Y.; Ogino, T. Host Cell Prediction of Exosomes Using Morphological Features on Solid Surfaces Analyzed by Machine Learning. *J. Phys. Chem. B* **2018**, *122* (23), 6224–6235. https://doi.org/10.1021/acs.jpcb.8b01646.

(116) Sokolov, I. On Machine Learning Analysis of Atomic Force Microscopy Images for Image Classification, Sample Surface Recognition. *Phys. Chem. Chem. Phys.* **2024**, 10.1039.D3CP05673B. https://doi.org/10.1039/D3CP05673B.

(117) Minelli, E.; Ciasca, G.; Sassun, T. E.; Antonelli, M.; Palmieri, V.; Papi, M.; Maulucci, G.; Santoro, A.; Giangaspero, F.; Delfini, R.; Campi, G.; De Spirito, M. A Fully-Automated Neural Network Analysis of AFM Force-Distance Curves for Cancer Tissue Diagnosis. *Applied Physics Letters* **2017**, *111* (14), 143701. https://doi.org/10.1063/1.4996300.

(118) Ciasca, G.; Mazzini, A.; Sassun, T. E.; Nardini, M.; Minelli, E.; Papi, M.; Palmieri, V.; de Spirito, M. Efficient Spatial Sampling for AFM-Based Cancer Diagnostics: A Comparison between Neural Networks and Conventional Data Analysis. *Condensed Matter* **2019**, *4* (2), 58. https://doi.org/10.3390/condmat4020058.

(119) Sokolov, I.; Dokukin, M. E.; Kalaparthi, V.; Miljkovic, M.; Wang, A.; Seigne, J. D.; Grivas, P.; Demidenko, E. Noninvasive Diagnostic Imaging Using Machine-Learning Analysis of Nanoresolution Images of Cell Surfaces: Detection of Bladder Cancer. *Proceedings of the National Academy of Sciences* **2018**, *115* (51), 12920–12925. https://doi.org/10.1073/pnas.1816459115.

(120) Petrov, M.; Sokolov, I. Identification of Geometrical Features of Cell Surface Responsible for Cancer Aggressiveness: Machine Learning Analysis of Atomic Force Microscopy Images of Human Colorectal Epithelial Cells. *Biomedicines* **2023**, *11* (1), 191. https://doi.org/10.3390/biomedicines11010191.

(121) Roduit, C.; Saha, B.; Alonso-Sarduy, L.; Volterra, A.; Dietler, G.; Kasas, S. OpenFovea: Open-Source AFM Data Processing Software. *Nat Methods* **2012**, *9* (8), 774–775. https://doi.org/10.1038/nmeth.2112.

(122) Jha, R.; Agarwal, A. Software (GUI/APP) for Developing AI-Based Models Capable of Predicting Load-Displacement Curve and AFM Image during Nanoindentation. *Coatings* **2021**, *11* (3), 299. https://doi.org/10.3390/coatings11030299.

(123) Braunsmann, C.; Schäffer, T. E. Note: Artificial Neural Networks for the Automated Analysis of Force Map Data in Atomic Force Microscopy. *Review of Scientific Instruments* **2014**, *85* (5), 056104. https://doi.org/10.1063/1.4876485.

(124) Müller, P.; Abuhattum, S.; Möllmert, S.; Ulbricht, E.; Taubenberger, A. V.; Guck, J. Nanite: Using Machine Learning to Assess the Quality of Atomic Force Microscopy-Enabled Nano-Indentation Data. *BMC Bioinformatics* **2019**, *20* (1), 465. https://doi.org/10.1186/s12859-019-3010-3.

(125) Chang, Y.-R.; Raghunathan, V. K.; Garland, S. P.; Morgan, J. T.; Russell, P.; Murphy, C. J. Automated AFM Force Curve Analysis for Determining Elastic Modulus of Biomaterials and Biological Samples. *J Mech Behav Biomed Mater* **2014**, *37*, 209–218. https://doi.org/10.1016/j.jmbbm.2014.05.027.

(126) Sotres, J.; Boyd, H.; Gonzalez-Martinez, J. F. Locating Critical Events in AFM Force Measurements by Means of One-Dimensional Convolutional Neural Networks. *Sci Rep* **2022**, *12*, 12995. https://doi.org/10.1038/s41598-022-17124-z.

(127) Nguyen, L. T. P.; Liu, B. H. Machine Learning Approach for Reducing Uncertainty in AFM Nanomechanical Measurements through Selection of Appropriate Contact Model. *European Journal of Mechanics - A/Solids* **2022**, *94*, 104579. https://doi.org/10.1016/j.euromechsol.2022.104579.
(128) Zhu, X.; Qin, R.; Qu, K.; Wang, Z.; Zhao, X.; Xu, W. Atomic Force Microscopy-Based Assessment of Multimechanical Cellular Properties for Classification of Graded Bladder Cancer Cells and Cancer Early Diagnosis Using Machine Learning Analysis. *Acta Biomaterialia* **2023**, *158*, 358-373. https://doi.org/10.1016/j.actbio.2022.12.035.

(129) Tian, Y.; Lin, W.; Qu, K.; Wang, Z.; Zhu, X. Insights into Cell Classification Based on Combination of Multiple Cellular Mechanical Phenotypes by Using Machine Learning Algorithm. *Journal of the Mechanical Behavior of Biomedical Materials* **2022**, *128*, 105097. https://doi.org/10.1016/j.jmbbm.2022.105097.

(130) Hedjazi, L.; Aguilar-Martin, J.; Le Lann, M.-V.; Kempowsky-Hamon, T. Membership-Margin Based Feature Selection for Mixed Type and High-Dimensional Data: Theory and Applications. *Information Sciences* **2015**, *322*, 174-196. https://doi.org/10.1016/j.ins.2015.06.007.

(131) Hedjazi, L.; Aguilar-Martin, J.; Le Lann, M.-V.; Kempowsky, T. Towards a Unified Principle for Reasoning about Heterogeneous Data: A Fuzzy Logic Framework. *Int. J. Unc. Fuzz. Knowl. Based Syst.* **2012**, *20* (02), 281-302. https://doi.org/10.1142/S0218488512500146.

(132) Hedjazi, L.; Aguilar-Martin, J.; Le Lann, M.-V. Similarity-Margin Based Feature Selection for Symbolic Interval Data. *Pattern Recognition Letters* **2011**, *32*(4), 578-585. https://doi.org/10.1016/j.patrec.2010.11.018.

(133) Kempowsky-Hamon, T.; Valle, C.; Lacroix-Triki, M.; Hedjazi, L.; Trouilh, L.; Lamarre, S.; Labourdette, D.; Roger, L.; Mhamdi, L.; Dalenc, F.; Filleron, T.; Favre, G.; François, J.-M.; Le Lann, M.-V.; Anton-Leberre, V. Fuzzy Logic Selection as a New Reliable Tool to Identify Molecular Grade Signatures in Breast Cancer - the INNODIAG Study. *BMC Medical Genomics* **2015**, *8* (1), 3. https://doi.org/10.1186/s12920-015-0077-1.

(134) Wu, H.; Zhang, L.; Zhao, B.; Yang, W.; Galluzzi, M. Deep Learning Strategy for Small Dataset from Atomic Force Microscopy Mechano-Imaging on Macrophages Phenotypes. *Front Bioeng Biotechnol* **2023**, *11*, 1259979. https://doi.org/10.3389/fbioe.2023.1259979.

(135) M. Nascimento, D.; M. Colombari, F.; Focassio, B.; R. Schleder, G.; R. Costa, C. A.; A. Biffe, C.; Y. Ling, L.; F. Gouveia, R.; Strauss, M.; M. Rocha, G. J.; Leite, E.; Fazzio, A.; B. Capaz, R.; Driemeier, C.; S. Bernardes, J. How Lignin Sticks to Cellulose—Insights from Atomic Force Microscopy Enhanced by Machine-Learning Analysis and Molecular Dynamics Simulations. *Nanoscale* **2022**, *14* (47), 17561-17570. https://doi.org/10.1039/D2NR05541D.

(136) Ilieva, N. I.; Galvanetto, N.; Allegra, M.; Brucale, M.; Laio, A. Automatic Classification of Single-Molecule Force Spectroscopy Traces from Heterogeneous Samples. *Bioinformatics* **2020**, *36*(20), 5014-5020. https://doi.org/10.1093/bioinformatics/btaa626.

(137) Huang, B.; Li, Z.; Li, J. An Artificial Intelligence Atomic Force Microscope Enabled by Machine Learning. *Nanoscale* **2018**, *10* (45), 21320-21326. https://doi.org/10.1039/C8NR06734A.

(138) Alwosheel, A.; van Cranenburgh, S.; Chorus, C. G. Is Your Dataset Big Enough? Sample Size Requirements When Using Artificial Neural Networks for Discrete Choice Analysis. *Journal of Choice Modelling* **2018**, *28*, 167-182. https://doi.org/10.1016/j.jocm.2018.07.002.

(139) Guillaume-Gentil, O.; Potthoff, E.; Ossola, D.; Franz, C. M.; Zambelli, T.; Vorholt, J. A. Force-Controlled Manipulation of Single Cells: From AFM to FluidFM. *Trends in Biotechnology* **2014**, *32*(7), 381-388. https://doi.org/10.1016/j.tibtech.2014.04.008.

(140) Cortes, C.; Vapnik, V. Support-Vector Networks. *Mach Learn* **1995**, *20*(3), 273-297. https://doi.org/10.1007/BF00994018.
# Didactic Box #1: Artificial Intelligence – An overview

<figure><img src="image_13.png" /></figure>

## Artificial Intelligence (AI) versus Machine Learning (ML) versus Deep Learning (DL)

AI encompasses all methods and techniques used to imbue computers or machines with intelligence. ML is a subset of AI, focusing on algorithms that enable systems to learn from data without explicit programming. And within ML, DL stands out for its utilization of neural networks, which are structured to mimic the way the human brain processes information, allowing for more complex learning and decision-making capabilities. (Fig. 1).

### Machine Learning vs Deep Learning

In ML, feature extraction is typically performed before the training phase using manual methods or predefined algorithms. Conversely, in DL, feature extraction is integrated into the learning process, where neural networks autonomously learn relevant features directly from the raw data, eliminating the need for manual feature engineering

## The need for Big Data of excellent quality and open

Generally, traditional Machine Learning algorithms often require hundreds of samples to achieve good performance, whereas Deep Learning models typically require even larger datasets, often on the order of thousands of samples for each class, to generalize effectively. This necessity for large amounts of data has led to the concept of "Big Data," where the focus is on generating, collecting, and processing vast volumes of data. However, it's not just about quantity; data quality is equally important. Before feeding data into either ML or DL models, it's essential to perform tasks like labeling, cleaning, and preprocessing to ensure that the data is accurate, relevant, and suitable for training. These preprocessing steps are crucial for enhancing the performance and robustness of the models, regardless of the approach used. Data sharing plays a significant role in advancing research and fostering collaboration in the field of AI. By making datasets publicly available, researchers can address the issue of data scarcity and enable others to reproduce results, validate findings, and develop new methods and algorithms. Open-sourcing not only the data but also the algorithms and model parameters used in research promotes transparency and reproducibility.

**Data augmentation** is a powerful technique used to artificially increase the size of a dataset, particularly when the available dataset is small. This method involves applying a variety of transformations or modifications to existing data samples to generate new ones. In the context of image data, these transformations can include rotation, flipping, scaling, translation, cropping, adding noise, adjusting brightness or contrast, and applying various filters.

<figure><img src="image_14.png" /></figure>

**The type of learning** indeed depends on the nature of the data and the task at hand.

In **supervised learning**, the dataset consists of input-output pairs, where each input is associated with a corresponding output label. The goal is to learn a mapping or relationship between inputs and outputs, enabling the model to predict the correct output for new, unseen inputs.

In **semi-supervised learning** deals with datasets where only a portion of the data is labeled, while the majority remains unlabeled. The model leverages both labeled and unlabeled data to improve performance, often by learning a representation of the data that captures underlying structures and patterns.
**Unsupervised learning** involves learning from datasets where the data points are not labeled. The goal is to uncover hidden patterns, structures, or relationships within the data without explicit guidance. Common tasks include clustering, dimensionality reduction, and density estimation.

**Reinforcement learning** is a type of learning where an agent learns to make decisions by interacting with an environment. The agent receives feedback in the form of rewards or penalties based on its actions, with the objective of maximizing cumulative reward over time. Reinforcement learning is well-suited for sequential decision-making tasks and has applications in areas like robotics, gaming, and autonomous systems.

These different types of learning cater to a wide range of problem domains and allow for the development of diverse and sophisticated models tailored to specific requirements and challenges.

## AI techniques and their applications

**In classification tasks**, the goal is to predict the category or class label of an input data point. This is typically done by learning a decision boundary that separates different classes in the input space. Classification is used for image recognition, sentiment analysis, spam detection.

**Regression tasks** involve predicting a continuous value or quantity based on input features. The goal is to learn a mapping from input variables to output values that best fits the data. Regression is used in fields such as finance, healthcare, and economics for tasks like stock price prediction, disease prognosis, and sales forecasting.

**Clustering tasks** aim to partition a dataset into groups or clusters such that data points within the same cluster are more similar to each other than to those in other clusters. Clustering is an unsupervised learning task and is used for tasks like customer segmentation, image segmentation, and anomaly detection.

Beyond classification, regression, and clustering, AI techniques are applied to a variety of advanced tasks such as object detection, image segmentation, natural language processing, and recommendation systems. These tasks often involve more complex data structures and require specialized algorithms and techniques tailored to the specific problem domain.
# Didactic Box #2 : Overview of AI Models:
## Types and Uses

<figure><img src="image_15.png" /></figure>

**Support Vector Machines (SVM)** in 1964, Vladimir Vapnik created the Support Vector Machines. This model is the one of the most used in Machine Learning because it's simple, fast and easy to understand¹⁴⁰. SVMs are supervised learning models used primarily for classification. They work by finding the hyperplane that best separates data from different classes in a high-dimensional space. SVMs can also address regression tasks. Initially, SVM algorithms construct hyperplanes to separate classes effectively, favoring those with minimal classification errors. While SVMs inherently handle linear problems, they can address nonlinear challenges through kernel tricks, extending their applicability.

**Decision trees, random forest, extra trees, gradient boosting tree...**
**Decision Trees** are a non-parametric supervised machine learning algorithm that make decisions by formulating a series of questions about the characteristics of the data. A tree begins with a root node, which initially serves as a decision node and symbolizes the entire dataset. Subsequently, the root node splits into child nodes, branching based on different features, thus segmenting the data proportionally. These nodes, also known as decision nodes, reflect the distribution of data within each subset. As the tree progresses, further splits occur until reaching terminal nodes, or leaf nodes. These leaf nodes encapsulate all potential outcomes in the dataset. For instance, in classification tasks, each leaf node corresponds to a distinct class in the dataset. **Random forests** are sets of decision trees that combine the predictions of several trees to obtain more robust and accurate predictions. **Extra Trees, or Extremely Randomized Trees**, are similar to Random Forests but introduce additional randomness during the construction of each tree. Instead of selecting the best split at each node based on feature optimization, Extra Trees randomly choose splits, which can lead to faster training times and potentially better generalization. **Gradient Boosting Trees** is a powerful ensemble learning technique that builds an ensemble of weak learners, typically decision trees, in a sequential manner. Each new tree is trained to correct the errors made by the previous ones, with a focus on minimizing the loss function. Gradient Boosting Trees often achieve state-of-the-art performance on various machine learning tasks and are widely used in competitions and real-world applications.

<figure><img src="image_16.png" /></figure>

This image represents the schematic of a decision tree with 2 features (X1, X2) and 2 output classes (Success or Failure). The decision tree consists of 3 decision nodes and 4 leaves (outputs). For example, here, the classifier will answer the question "Can I win the game?" and our data are X1 = 20 and X2 = 1. So, 1st node, X1 > 10 = Yes. Therefore, 2nd node, X2 < 5 = Yes, the output of the decision tree will be Success.

**Multiple Layer Perceptron (MLP): Artificial Neuron networks (ANN), Convolutional Neuron networks (CNN), Recurrent neural networks (RNN)...**

**Artificial neural networks** are models inspired by the functioning of the human brain. They are composed of several layers of interconnected neurons, and during training, they learn from examples by adjusting the weights of connections between neurons to minimize the difference between predicted and actual outputs. This process, known as backpropagation, involves iteratively updating
the network's parameters using optimization algorithms like gradient descent. ANNs are used for a wide variety of tasks, including classification, regression, image and text recognition, among others. CNNs are a variant of MLPs, designed specifically for image processing. They are capable of learning visual features and patterns at different scales, making them highly effective for tasks such as image classification, object detection and image segmentation. CNNs are trained using large datasets through a process of backpropagation. In 2016, J. Redmon invented You Only Look Once (YOLO) which is used to accurately detect objects in a scene. A modified CNN divides the image into a grid and classifies each box of the grid into an object or another. RNNs are used to model data sequences, where each element of the sequence depends on the preceding elements. They are widely used in natural language processing, machine translation, text generation and other tasks involving sequential data.

<figure><img src="image_17.png" /></figure>

Key components of ANNs include:

- Input Layer: The input layer receives raw data or features and passes them to the subsequent layers for processing.

- Hidden Layers: Hidden layers are intermediate layers between the input and output layers. They perform complex transformations on the input data through weighted connections and activation functions. Deep neural networks contain multiple hidden layers, allowing for the extraction of hierarchical representations of the input data.

- Output Layer: The output layer produces the final predictions or outputs based on the processed information from the hidden layers. The number of neurons in the output layer depends on the nature of the task, such as classification (multiple neurons representing different classes) or regression (single neuron for continuous output).

These models represent only a small selection of the many architectures available in AI. The choice of model will depend on many factors, including the nature of your data, the complexity of the task and the computing resources available.