# Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation

Dimitris Visvikis, Philippe Lambin, Kim Beuschau Mauridsen, Roland Hustinx, Michael Lassmann, Christoph Rischpler, Kuangyu Shi, Jan Pruim

► To cite this version:

Dimitris Visvikis, Philippe Lambin, Kim Beuschau Mauridsen, Roland Hustinx, Michael Lassmann, et al.. Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation. European Journal of Nuclear Medicine and Molecular Imaging, 2022, 49, pp.4452-4463. 10.1007/s00259-022-05891-w. hal-05017099

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# Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation

Dimitris Visvikis<fnref n="1" /> · Philippe Lambin<fnref n="2" />,<fnref n="3" /> · Kim Beuschau Mauridsen<fnref n="4" />,<fnref n="5" /> · Roland Hustinx<fnref n="6" /> · Michael Lassmann<fnref n="7" /> · Christoph Rischpler<fnref n="8" /> · Kuangyu Shi<fnref n="5" />,<fnref n="9" /> · Jan Pruim<fnref n="10" />

Received: 14 January 2022 / Accepted: 25 June 2022 / Published online: 9 July 2022

© The Author(s) 2022

## Abstract

Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as it will in everyday life. In this review, we focus on the potential applications of AI in the field, both from a physical (radiomics, underlying statistics, image reconstruction and data analysis) and a clinical (neurology, cardiology, oncology) perspective. Challenges for transferability from research to clinical practice are being discussed as is the concept of explainable AI. Finally, we focus on the fields where challenges should be set out to introduce AI in the field of nuclear medicine and molecular imaging in a reliable manner.

**Keywords** Artificial intelligence · Nuclear medicine · Molecular imaging

## Introduction

The applications of artificial intelligence (AI) in healthcare are potentially numerous, clearly going beyond the field of medical imaging alone. AI is not a new scientific discipline, having its origins in the Dartmouth conference of 1956 [1]. Although multiple layer perceptrons (MLPs), considered as the origin of “deep” convolutional neural networks (CNNs),

were studied since the 70’s, major developments came in the 90’s [2] and early 00’s [3] concerning the learning rules establishing how weights in MLP can be updated. AlexNet [4], winning in 2012 the ImageNet competition performing visual object recognition from photographs, introduced a major breakthrough in neural network performance bringing AI to the forefront of interest on computer vision and imaging applications. A more detailed historical overview can be found elsewhere [5]. Growing numbers of patients, higher demands for quality like early detection and personalized therapies and an increasing workload for medical

This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence)

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Jan Pruim
j.pruim@umcg.nl

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<footnote n="1">¹ LaTIM, INSERM, UMR 1101, University of Brest, Brest, France</footnote>

<footnote n="2">² The D-Lab, Department of Precision Medicine, GROW – School for Oncology, Maastricht University Medical Center (MUMC +), Maastricht, The Netherlands</footnote>

<footnote n="3">³ Department of Radiology and Nuclear Medicine, GROW – School for Oncology, Maastricht University Medical Center (MUMC +), Maastricht, The Netherlands</footnote>

<footnote n="4">⁴ Center of Functionally Integrative Neuroscience and MindLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark</footnote>

<footnote n="5">⁵ Department of Nuclear Medicine, University of Bern, Bern, Switzerland</footnote>

<footnote n="6">⁶ GIGA-CRC in Vivo Imaging, University of Liège, GIGA, Avenue de l'Hôpital 11, 4000 Liege, Belgium</footnote>

<footnote n="7">⁷ Klinik Und Poliklinik Für Nuklearmedizin, Universitätsklinikum Würzburg, Würzburg, Germany</footnote>

<footnote n="8">⁸ Department of Nuclear Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany</footnote>

<footnote n="9">⁹ Department of Informatics, Technical University of Munich, Munich, Germany</footnote>

<footnote n="10">¹⁰ Medical Imaging Center, Dept. of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands</footnote>
and nursing staff creates a demand for automation and the need for extracting more information from acquired data. Potential advantages of AI are already visible in screening routines in which a high number of patients (and associated data) are investigated for the presence or absence of disease, with results that are not worse than human performance. For example, McKinney et al. were able to show non-inferiority of their algorithm for screening of breast cancer as compared to experienced radiologists [6]. At the same time, AI results are being criticized because of the lack of transparency and consequently a potential lack of reproducibility [7].

The introduction of AI into the operation of radiology departments has led to optimizing resources [8]. Such operational AI should prove even more relevant in nuclear medicine (NM), which deals with radioactive isotopes, whose shelf-life is limited. Patient scheduling, management of preparation of radiopharmaceuticals, report generation and recovering and organizing previous NM and imaging studies are examples of tasks where AI could contribute to streamlining the operation of a department.

We must however admit that AI still has little place within NM so far. No doubt, this may be related to the fact that smaller patient numbers pass through a NM department every day as compared to, e.g. a radiology department. However, this is underestimating the potential of AI as on the one hand, each patient image represents numerous—be it correlated—data [9], and on the other hand, AI methodology has been shown to be able to adjust to smaller datasets by utilizing knowledge obtained in larger ones.

In this paper, we provide a short and concise review of the current state of the art in the field for both more physically and more clinically oriented components of AI applications in imaging. For more detailed reviews on each specific topic, readers are directed to other, more specialized articles [10–17]. Finally, some ideas on the introduction and use of AI in NM are discussed.

## Definitions

Throughout the literature, different terms and phrases are being used to describe the underlying principles of AI. In a 2018 communication by an independent high-level expert group set up by the European Commission, artificial intelligence refers to systems that display intelligent behaviour by analysing their environment and taking actions — with some degree of autonomy — to achieve specific goals [18]. We refer the reader to this document or other recent articles for comprehensive definitions of machine learning, deep learning and neural networks [1].

## The basics of AI: statistics

It is sobering to consider how AI is connected to classical, established statistical modelling. At the same time, it claims a right of its own since it mitigates a number of often overlooked but critical weaknesses in classical statistical procedures which are commonly used in the medical literature. Normal practice is to test the possible difference in the value of a biomarker (e.g. image intensity reflecting blood flow, oxygen extraction or metabolism) across two groups or conditions using a t-test. This statistical procedure quantifies the (un-)likelihood of the experimental data arising from identical distributions. However, it does not provide insight to the differentiability of the two groups based on the value of the biomarker. If a biomarker is claimed to be an early marker of disease, it is less relevant at which statistical significance level the values differ between prodromal patients and a control group than it is as to how accurately the marker itself differentiates between groups. Hence, a low *p*-value can lead to an artificially high confidence in a biomarker's ability to actually detect disease. The statistical concept of *p*-values in itself must be interpreted with care because it tells little about how replicable a result is. In fact, it can be shown that if a statistical test yields a *p*-value of 0.05 (i.e. the standard level of significance), the probability of replicating this result is only 50% [19]. The probability increases only to 80% for a *p*-value ten times smaller, $p = 0.005$. Together with procedural flaws, such as serially adding variables to control for in regression, referred to as "researcher degrees of freedom," it leads to an inflation of false-positive results and the ongoing replication crisis [20, 21].

In contrast, machine learning (ML) conceptually aims to optimize accuracy and reproducibility and in the case of AI even avoids human procedural bias in parameter selection. Interestingly, a ML approach does not necessarily venture beyond classical statistical modelling, it only takes a different approach to interpretation.

Linear regression is a classical statistical model but may also be seen as one of the simplest types of ML. Whereas in classical applications the interest is in determining the effect of certain covariates on a target, often reduced to a *p*-value as outlined above, the ambition of a ML application is to ensure the best possible fit to the observed data, and therefore predictive performance, while at the same time optimizing replicability.

AI diverts from classical statistics where it promotes more complex models in order to increase predictive accuracy. In particular, in case of deep learning (DL), classical statistical models such as logistic regressions are merged into hierarchies in such a way that the results of initial logistic regressions are used as input to others in so-called
neural networks. This architecture implies that complex interactions between input variables are effectively utilized to increase model performance. It also implies that raw data, such as images, may naturally be input to the model, instead of summary features (such as for instance tumour volume, texture, etc.), which depend on human interaction.

Hence, AI, in comparison to classical statistical models, seeks to optimally identify feature differences between groups to optimize model performance while promoting generalizability beyond the study data initially available and has the ability to operate on raw healthcare data, such as images or health record information, without the need for manual feature extraction and summary, yielding a potentially more unbiased approach to knowledge discovery [22].

## Applications of AI in NM

For the sake of brevity and clarity, two major components in which AI can play a part can be discerned. The first component concerns image formation and image processing tasks and will be referred to as the “physics” component in the following sections. The second is largely application driven and hence will be referred to as the “clinical” component. It concerns routine workflow and final clinical endpoints, e.g. diagnosis, prognosis and prediction of response to therapy. Although these two components will be treated separately in this review, both are ultimately connected, the convolutional neural network (CNN) concepts being similar and very often associated within a single imaging paradigm. An example within this context may be the radiomics pipeline proposed by Hatt and Visvikis et al., where the development of AI-based image formation and segmentation algorithms serves to accurately determine regions of interest and extract imaging biomarkers which can subsequently be used in diagnosis and/or patient therapy stratification as well as follow-up [1, 23].

In addition, the available data from multimodality devices such as PET/CT or PET/MR and from the emerging total body PET technology is expected to largely increase with the development of (multi)parametric imaging. Within this context deep learning (DL)-based reconstruction and analysis, algorithms are potentially more efficient to deal with the increasing volumes of acquired data.

## Section 1:“Physics”

### Image reconstruction and data corrections

Following data acquisition, a conventional image reconstruction task involves the estimation of representation parameters for the accurate determination of the in vivo spatiotemporal radioactivity distribution. This involves the use of a

model which accounts for different aspects including scanner/detector geometry, the physics of the detection process, physiological motion, measured data noise, etc. Until the late nineties, image reconstruction in NM was based on filtered-back projection. In current clinical practice, it is an iterative process that involves a measure of the difference between the modelled and the measured data. In the process, the difference is minimized through successive iterations using an objective function of choice (Poisson log likelihood). The introduction of AI to this reconstruction process represents a fundamental paradigm shift. The measured data are now mapped to an estimate of the image that we would like to see as a result. This is achieved by learning a reconstruction operator using training data. Data need to be sufficiently diverse and variable to cover all possible different imaging possibilities.

In the so-called direct reconstruction, the training is performed between the raw data in the form of sinograms/projections and the reconstructed images [24], with newer approaches using generative adversarial networks originally proposed in the context of image-to-image translation [25]. In the second scenario, DL may intervene within the conventional iterative image reconstruction process by denoising the successive image estimates or by introducing data based on prior information throughout the iteration process. Hence, it facilitates a faster convergence combined with a more accurate final image estimation [26, 27]. So far, the majority of these works have taken place in PET imaging and less in SPECT imaging [28]. Future work will need to provide further evidence of the advantages of using DL for image reconstruction tasks, in terms of the (i) reconstructed image’s qualitative and quantitative accuracy compared to state-of-the-art iterative algorithms, but also (ii) in terms of speed of execution which should be largely improved with “direct reconstruction” DL approaches (once the algorithm has been trained). This latter point can have a significant impact within the context of total body PET imaging where the volume of collected data increases substantially relative to current clinical PET imaging devices. Also, it will be necessary to specifically compare their performance to that of post-processing reconstruction image improvements based on DL such as denoising or super-resolution tasks.

In terms of data corrections, the majority of the work has been focusing on the field of DL-based attenuation correction (AC) and less on scatter correction (SC). The large majority of the proposed AC approaches are based on a wider image-to-image translation (also known as “image generation”) field of DL which is a generic task of AI [14]. The objectives of these developments are to produce CT equivalent-based AC maps from MR images in multimodality PET/MR [29], improve the attenuation maps produced by maximum likelihood reconstruction of activity and attenuation (MLAA) approaches [30] or totally remove the need

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for anatomical image information by directly producing
attenuation-corrected images from non-attenuation-cor-
rected PET data [31]. In terms of SC, the scatter sinograms
could be produced from emission and attenuation raw data in
PET or SPECT imaging [32, 33] or the direct production of
scatter-corrected images (usually combined with AC) with
non-corrected PET images being used as the input data to
the network [34].

These developments show great promise, but there is
a clear need for the evaluation of the robustness of these
approaches both in terms of variability in anatomical loca-
tion as well as presence of abnormalities, a study that can
only be achieved by considering larger patient populations.

Image processing and analysis

Again, one can identify two main areas: the one dealing
with a post-processing improvement of the reconstructed
images (remove noise, improve resolution) and the second
with a further analysis of the data that allows the extraction
of imaging biomarkers which subsequently can be used for
different clinical endpoints [5]. Denoising primarily aims at
pushing the limits of low-dose NM imaging and is based on
the use of the low dose standard reconstructed PET images
alone or in combination with anatomical modality images
(predominantly MRI) as an input [26, 35]. DL-based super-
resolution aims at obtaining a high-resolution image from
a low-resolution input image. Approaches rely on training
that uses either paired low/high-resolution NM images or
combined low-resolution PET and high-resolution MR
images [36]. Again, results are promising, but most studies
lack a comparison with current state-of-the-art denoising
techniques.

Concerning NM image analysis, the most significant
developments deal with the field of image segmentation.
NM images suffer from low spatial resolution and—as a
result—partial volume effects which render the segmenta-
tion of functional volumes of interest a challenging task
relative to anatomical image-based ROI definition. The first
signs of the potential performance of DL-based segmenta-
tion algorithms relative to the current state-of-the-art have
started emerging in the early segmentation software chal-
lenges organized in 2016 [37]. In this work, 150 acquired
and simulated physical phantom datasets and 25 delineated
3D tumour volumes in PET images were used for training
purposes. The CNN-based algorithm led to superior preci-
sion in the segmentation results, statistically outperforming
9 out of the 12 other segmentation approaches participating
in the challenge, some of them among the current state-of-
the-art. More recent results suggest that a 3D U-net type
architecture based on encoder-decoder layers can provide
high-accuracy functional tumour volume segmentation
results from PET images in different cancer models and

locations without the need for any user intervention [5]. In
practice, however, results are not optimal yet. In a recent
study in patients with cervical cancer by Pinochet et al., it
became clear that the segmentations and total metabolic
tumour volumes determined by their algorithm needed to
be verified and, sometimes, even corrected to be similar to
the manual segmentations [38]. Future direction of research
in this field will concern the exploitation of multimodality
datasets within the context of automatic image segmenta-
tion combining PET, CT and MR whenever available [39].
Almost all works in the field of image reconstruction and
analysis referred to above have been carried out considering
static acquisitions, but all of them can be equally applied to
dynamic imaging, including direct parametric PET image
reconstruction [40].

Finally, another significant future application for NM
imaging, within the wider area of DL-based image synthesis
(mentioned in the data correction section above), is that of
image harmonization. Multicentre trials will become even
more important in the future given the requirements for large
training datasets in AI developments. Within this context,
it is essential to ensure the development of harmonization
approaches which go beyond acquisition and processing
standardization protocols that minimize but do not elimi-
nate imaging technology-related variability or otherwise
known as the “centre effect” [41]. Early evidence suggests
that DL, and more specifically the field of image-to-image
translation using generative adversarial networks (GANs),
can play a major role in image-based harmonization meth-
odologies [42].

A field where AI may play an important role also is image
registration. Despite the fact that in clinical practice there
is already a strong clinical multimodality component (PET/
CT, SPECT/CT, PET/MR) in use, the application of AI in
image registration can be quite relevant. Consider longitu-
dinal studies but also the alignment of PET and CT data
within a single PET-CT study. Patients move, and there is
always some mismatch between CT and PET, either random,
systematic or cyclic. Also, AI may help in metal artifact
reduction strategies for improved attenuation correction in
hybrid PET/CT imaging.

**Section 2 — “Clinical”**

Neurology

Neurodegenerative disease is a major application of NM.
It is common experience that reading these brain images
(FDG-PET, DaT-scan, amyloid PET) can be quite challeng-
ing. Consequently, aid from AI has been sought. In a proof-
of-concept study, Kim et al. described the use of a network
in discriminating between Parkinson’s disease and a normal
distribution of ¹²³I-ioflupane. Although the study was relatively small (118 abnormal and 63 normal scans) and of a retrospective character, they were able to show a high test accuracy with a ROC of 0.87 [43]. Choi et al. built a DL model for discriminating the FDG uptake patterns in Alzheimer’s from normal controls. That model was then transferred to images of patients with mild cognitive impairment to see if it could discriminate those patients that would rapidly convert to Alzheimer’s disease from others. Also, they studied whether patients with Parkinson’s dementia could be discerned. They concluded that their DL-model could successfully be transferred to multiple disease domains [44]. Similarly, Son et al. trained a DL system in interpreting ¹⁸F-florbetaben images and concluded that in visually equivocal scans, their algorithm was able to discriminate between progressive disease and negative cases, thus providing information on clinical outcome and even prognosis assessment [45]. Finally, the use of deep learning algorithms may possibly expand FDG-PET/CT to other diseases, e.g. to discriminate between ALS and ALS mimics [46].

Such examples from the literature show the potential of AI in relatively small patient population studies. Thorough validation and finetuning on larger patient populations are needed to facilitate clinical translation.

## Cardiology

Myocardial perfusion imaging (MPI) using SPECT or PET is the most often performed scan in nuclear cardiology. The aim of this examination is to help the clinician to determine whether the patient suffers from cardiac disease (usually coronary artery disease), whether treatment is necessary and, if so, which therapy is preferred (revascularization vs. optimal medical treatment). The most commonly used modality is SPECT using Tc-99 m-labeled perfusion tracers. Due to the high number of examinations and the relatively standardized procedure of the test, myocardial perfusion imaging using SPECT is an interesting model for applying AI. The examination is usually carried out on a hybrid device, so that a CT dataset (or in the case of PET sometimes an MRI data set) is also generated.

The first applications of AI in nuclear cardiology were the segmentation of imaging data, the diagnosis of obstructive coronary artery disease and the prediction of major adverse cardiac events (MACE). It has already been shown that the automatic determination of the valve plane — an important step in the reconstruction of image data, often requiring user interaction with the respective software — was performed by an AI approach in a manner comparable to highly experienced NM specialists [47]. Also, a work on datasets of almost 1000 patients showed that a fully automated analysis of the MPI studies showed a good agreement with experts [48]. In another study, besides the categorization of patients

based on MPI data regarding the presence of obstructive CAD, parameters were automatically selected from 33 clinical and quantitative data with an information gain [49]. Using this information, a prediction model was then trained with the help of the tenfold cross-validation technique, and the results were compared with the assessment of two experts, whereby the machine learning algorithm was not inferior. Similar results could be reproduced in other studies [50, 51]. The last major field of application is the attempt to prognosticate the further course of the disease. In contrast to the normal (human) approach where mainly the imaging data are used, AI allows the consideration of a large number of variables. In a work on more than 2500 patients, an ML algorithm for the prediction of MACE was trained and evaluated [52]. A total of 70 clinical, ECG or imaging parameters were provided to the algorithm. The prediction of MACE was significantly better when these parameters were used in combination rather than when only the imaging parameters were provided by the algorithm. Interestingly, the machine learning approach was even better than the interpreting physicians, who had access to all patient data [52].

These are promising examples to the use of AI in nuclear cardiology. Further developments are expected, e.g. in the reconstruction of imaging data. And it is also expected that there will be AI-based software tools available in the near future to support physicians in the interpretation of imaging data in everyday clinical routine. A position paper on the applications of AI for NM multimodality cardiovascular imaging has been recently published by EANM and EACVI [53].

## Oncology

In oncology, the movement towards personalized medicine is most clearly visible. It should tailor the treatment towards the genetic composition of the tumour(s) and take into account the extent of the disease and the condition of the patient. It is generally believed that AI will help in this process.

This is not limited to positron emission tomography alone. One of the least expected areas to introduce AI is in classic bone scanning with [⁹⁹mTc]Tc-labelled diphosphonates. However, bone scanning is much used, and interpretation can be difficult at times. Zhao et al. developed a deep neural network algorithm based on over 9500 planar bone scans, validated it on over 1250 cases and tested it also in over 1250 cases. Then, a competition was held between AI and 3 experienced NM specialists. In the initial part, AI reached to an overall diagnostic accuracy of 93%. In the second part, the algorithm performed slightly better than the physicians, proving non-inferiority. Time gain was enormous: the AI took just over 11 s to analyse, whereas the 3 physicians took on average 2 h and 15 min for the task [54].

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Different algorithms for this task are being tested currently, and the early results show that despite the differences in approach, outcomes are quite comparable [55]. However, one also has to realize that a thorough validation in case of bone scanning is very difficult as a gold standard is lacking. Also, the algorithms used should be open to the public, lest they can be checked and challenged by others.

Another promising field of the application of AI is in response evaluation with ¹⁸F-FDG-PET/CT through extracting quantitative information (“radiomics”) from the images, which will be discussed in more detail below [56]. Very interesting is the hypothesis that the extraction of radiomics features may endorse a histological diagnosis or even replace a histological diagnosis when it is difficult or often impossible to get a biopsy [57]. A suggestion that goes beyond FDG, but to date, a very limited number of radiomics studies have considered other tracers.

## Diagnosis, radiomics, response to therapy and dosimetry

Traditionally, the extraction of “handcrafted radiomics” requires manual segmentation of the region of interest. Subsequently, thousands of human-defined and curated quantitative features from the ROI which for example describe tumour shape, intensity and texture are extracted [57–59]. As already mentioned in the “physics” section above, all of these steps can be performed by using AI. In terms of classification and model building, machine learning techniques have been used over the past 30+ years with variable levels of success and have been recently evaluated within the context of radiomics [60]. The objective is to identify those imaging features that are associated or causally related with a given clinical or biological endpoint. Latest evidence suggests that a consensus based on the results provided by multiple such techniques could improve a model’s prediction performance and reduce the variability of the results observed [61–63]. One current trend with DL involves the “direct model” building based on the use of identified/segmented volumes without having to explicitly extract radiomics features, a process known as the use of “deep features” [64, 65]. Preliminary works exist extracting these deep features from whole body images within the region of interest and associated segmentation steps embedded in the overall process. If successful, such an approach could allow the elimination of the time-consuming lesion identification and annotation/segmentation step, facilitating the introduction of image-derived features in the clinical workflow for diagnostic and therapy stratification purposes. One major challenge in such data-intensive AI applications is the need to exploit multicentre data, an essential step in bringing into routine clinical workflow single centre predictive and prognostic models in the future. The use of multicentre trials is

associated with increased variability due to different image reconstruction protocols implemented on different manufacturer equipment as well as non-standardised data acquisition protocols. The approaches to handle such variability are either (i) based on allowing an AI model to learn from it or (ii) develop image harmonisation approaches based on AI, a field currently very much in the forefront of methodological developments [66].

Another area of application where AI could play a significant role is related to radionuclide dosimetry. Especially in children and young adults undergoing repeated scans, dosimetry and dose reduction are an issue. In a recent study by Wang et al., they showed that the introduction of AI has a potential to reduce the FDG-dosage up to a 1/8 dose equivalent (0.18 MBq/kg) yet providing interpretable images. SUV was not affected [67].

Based on a series of planar or 3D images, different dosimetry methods have been developed to calculate the absorbed dose distribution, which in turn can be used to quantify the whole-body dosimetry of the therapeutic agent and to support treatment planning [68]. Despite the potential of quantifying a companion diagnostic tracer for underpinning radioligand therapy (RLT) treatment planning, the estimation and extrapolation of the pharmacokinetics are associated with certain practical difficulties. Furthermore, the estimation of pretherapy pharmacokinetics normally requires multiple whole body dynamic scanning repeatedly for as long as possible. Although these issues may be resolved with the development of total body PET systems, such protocols are currently incompatible with routine clinical practice. Because of these technical limitations, pre-therapy imaging is usually only used to qualitatively select candidates for RLT and to rule out obvious risks. The patients are still treated with a fixed radiopharmaceutical activity [69]. AI may unveil the complex relationship between pre-therapy patient data, such as imaging, demographic data and laboratory data and the radiation dose distribution to be obtained during therapy. To illustrate this, a DL method was developed to automatically detect and segment prostate cancer lesions on ⁶⁸Ga-PSMA PET/CT-images [70], which may support the definition of the treatment targets for the planning. Furthermore, the potential of AI in predicting organ-wise or voxel-wise post-therapy dosimetry of radiopharmaceuticals has also been shown [71]. GANs have been developed to discover the complex relationship between pre-therapy patient data and post-therapy radiation dose distribution and therefore may predict the voxel-wise dose distribution for treatment planning. Finally, training of an AI dosimetry prediction model requires accurate dose estimation. DL methods have been developed to replace Monte-Carlo simulation for the estimation of voxel-wise dosimetry [71, 72]. Initial results indicate that these methods are computationally efficient to consider individual tissue density distributions as well as
heterogeneity of the concentration of the radiopharmaceutical to improve dosimetry estimation from SPECT or PET measurements.

In summary, using AI in NM multimodality imaging has the potential to play a significant role in routine diagnosis but also in the more challenging personalised patient therapy monitoring and response assessment applications. Within this context, AI may play a role in the development of new radiotracers for imaging and therapeutic purposes [73]. In addition, AI can facilitate accurate voxel-wise dose calculations for dosimetry purposes in radionuclide therapy. Although the development of large axial field of view PET scanners may bring dynamic and parametric whole-body imaging to the forefront, the application of AI in this field is currently in its infancy, and the associated clinical interest remains to be demonstrated.

## Challenges for transferability from research to clinical practice

Despite the promises of AI, some of them outlined above, there are also clear limitations in terms of their implementation and most importantly in terms of their clinical acceptability. A recent review by Torres-Velazquez et al. discussed in detail all of these challenges for the use of DL in medical imaging [5].

First, there is the level of performance, as well as its reproducibility and robustness to variable imaging protocols and devices, which depends on the quantity and on the quality of the available data for training of the system. In addition, once the training of an algorithm has been completed, datasets that were not used in the training are needed to ensure validation and testing processes. Irrespective of the step (training, validation, testing) in the process, all data need to be accurately annotated since AI learns from this annotation and therefore depends largely on the precision and consistency of the available data. This cannot be emphasized enough and is a plea for structured reporting. Another solution here will be the use of consensus annotation from multiple observers/operators in order to reduce the potential impact of a single reduced quality annotation. This is critical for applications such as lesion detection tasks. Using actual clinical progression or outcome is a solution for other problems, such as predicting disease progression and response to therapy. In addition, quality control protocols for the evaluation of such annotations should be put in place. Finally, in combination with standardization initiatives, image-based harmonization approaches as mentioned above hold the potential to eliminate the impact of a ‘centre-effect’ by bringing data from multicentre trials together in an efficient manner for AI training purposes.

The development of AI normally depends on the availability of a large amount of data. Compared to other disciplines, it is usually challenging to gather such large cohorts of patients in NM. Apart from the variations of scanners or imaging protocols, NM imaging and therapy have additional variations in radiopharmaceuticals and dosages. This large complexity makes NM studies susceptible to incomplete or missing data. As a result, it hampers extensive development of AI methods in NM. There are some potential ways out. Physiologically based pharmacokinetic modelling may assist the translation of different radiopharmaceuticals during AI development [74]. And weakly supervised deep learning may accelerate the learning on incomplete datasets [75].

Another issue is the variability with respect to the targeted task. Lack of variability will lead to overfitting of the training data and poor generalizability. The developed approaches will work during training but demonstrate reduced performance during the validation/testing steps. Therefore, cross validation is a *conditio sine qua non* when developing models. But beyond this extensive clinical testing is necessary. In general terms, a minimum of 70% training and 30% validation is used. In the cross-validation approach, the k-fold cross-validation is the one most frequently used [5]. Data augmentation can be used in certain cases by e.g. shifting, zooming or rotating images [76]. Be aware that — in tasks with a clinical endpoint — such data augmentation approaches may be less efficient than when used for more physics-oriented tasks. Another approach is ‘transfer learning’, where the context of the specific issue of data quantity involves a first-level training of the AI on other types of available data not specific to the task in hand, with a fine-tuning step using the task-specific datasets. The same concept can be used also for domain adaptation where a successful network within a given task can be used in another domain (e.g. moving from PET to SPECT image segmentation).

For the testing and validation of AI algorithms, including adherence to international and national standards, large datasets are needed. This inevitably means (standardized) multicentre cooperation and sharing of data, certainly within the field of NM and molecular imaging. Given the difficulty in data sharing, e.g. due to the Directive 95/46/EC (General Data Protection Regulation) of the EU, different approaches can be highlighted and encouraged. One concerns the creation of large, publicly available, open access image databases, such as for example ADNI or TCIA [77, 78], which preferably should be encouraged by public organisations. In this respect, the upcoming European Health Data Space is also eagerly awaited. Other variability aspects that need to be addressed within such publicly available repositories are the availability of external parties to the imaging data (such as demographics, omics, histopathology, outcome). Alternatively, the potential training of a given model could go

<figure><img src="image_6.png" /></figure>
through distributed training [79], where a model is further
refined by exploiting local databases without the need for the
exchange of datasets in a larger repository, hence reducing
reglementary-related obstacles. In both cases, strict cyber-
security protocols need to be adhered to, not only in terms
of data but also in terms of algorithm security in order to
ensure their widespread deployment, usage and reliability.

## Explainable AI

A key dimension to ensure the use of AI is user trust from
both the medical professional and the patient. They should
be able to understand the AI's answers (both in real-time
and afterwards) and should be able to determine when to
trust the AI and when the AI should be distrusted. This
will be an essential element for clinical transferability.
Current approaches to AI have a high 'black box' factor,
which is a clear drawback for clinical adoption. The dif-
ferent approaches need explainable/interpretable models
based on DL in order for clinicians to understand, interpret
and therefore trust such models. Different approaches have
been proposed, and their large-scale evaluation is clearly a
future challenge. Most of them involve the use of a retro-
propagation of information from the results to the input data
with different visualisation possibilities [79, 80].

With this in mind, the concept of Explainable AI (XAI) was introduced. XAI emphasizes the need that the response should be understandable: why did the algorithm arrive at a certain conclusion? Does it make sense? The right for explanation is seen as a basic requirement in our society and is also a request for certification (CE marked and FDA approved). Blind reliance on AI may lead to Kafka-like situations, as depicted in the New York Times bestseller 'Weapons of Math Destruction' by Cathy O'Neal [81]. Why should a patient accept receiving adjuvant chemotherapy if both the patient and the doctor do not understand the reasons for doing so?

There are 2 routes to approach this problem, viz. the route of the interpretable model or the route of the explainable model. An interpretable model provides its own explanation and can be intuitively understood, e.g. the risk of treatment-induced toxicity is higher in elderly patients. In contrast, an explainable model does not provide its own explanation. These models are black-box models and are typically too complicated to be understood by humans. Additional techniques or visualization tools (e.g. an activation map on an image) are needed to understand how the model made the predictions. This approach is also seen as a quality assessment. For example, if an AI model concludes that a lung nodule is malignant but the most informative image voxels are outside the nodule, then the likelihood of a false positive finding is higher.

Another consideration is *infobesity* (overload of information) whilst typically the human brain can handle a maximum of 5 variables to make a decision. The challenge is to have ‘glass box’ models that are explainable to a ‘human-in-the-loop’, without sacrificing AI performance. There is also the request from users to have a bidirectional flow of information, rather than the AI giving a conclusion unidirectionally. A frequent requirement is a user interface allowing to ask questions on the answers/predictions of the AI model, to put them into context, to compare to previous cases, etc. Methods that can provide qualitative (visual) feedback through the use of network visualization techniques using saliency/activation maps can be handy therefore to accompany these developments [64, 80]. This user interface could also be used several years later in e.g. retrospective evaluation of the AI performance, or in case of medico-legal issues.

Again, the European Union is taking up the gauntlet by
proposing a regulatory framework for AI (https://digital-strat
egy.ec.europa.eu/en/policies/regulatory-framework-ai) with
(a.o.) the goal that Europeans can trust what AI has to offer.

## Comparing performance of algorithms

As already mentioned earlier, over the past 3–4 years, there
have been numerous DL algorithms and associated pipelines
proposed for different tasks in NM imaging. One of the dif-
ficulties with DL algorithms are the hundreds of parameters
that need to be optimized but also the different types of net-
works and their implementation details in terms of e.g. the
number of layers and associated connections, optimizers and
loss functions that can be varied. Consequently, it is crucial
to compare the performance of all these various implemen-
tations under controlled conditions. Given all the potential
variations, it is impossible to reproduce results from the
literature by re-using the proposed approaches. Alterna-
tive verification methods are to be implemented, such as
the creation of software challenges. In these challenges, a
given dataset is made available for developers to evaluate the
performance of their algorithms within a controlled environ-
ment. Numerous software challenges have been organized
throughout the years, mainly within the field of image seg-
mentation [37], but also in other fields of interest increas-
ingly targeting clinical endpoints (https://grand-challenge.
org/challenges/). Whereas NM has been lagging behind, it
is the conviction of us that such challenges can and should
be organised in a multicentre set-up. Challenges should be
based on a predetermined categorisation of potential appli-
cations using different factors, such as impact and potential
utility and uptake in clinical practice. Different points may
be investigated by such challenges, such as for example:
1. Explain the need for data and associated requirements (volume, annotation, QC), definitions of training and validation datasets

2. Mention the dependency of data volumes needed on the targeted task, which also requires standardisation and harmonisation for the exploitation of multicentre datasets

3. Define algorithm-related aspects like the number of parameters, optimisation protocols, robustness, transferability on datasets from different instruments or body locations

4. Deal with model interpretability (white–grey box concept compared to black box), and thus with the acceptability by professionals (medical physicists, physicians) and the public (patients and families)

5. Integrate established domain knowledge (e.g. PBPK modelling) in AI algorithms or training procedure to reduce the requirement of data volume and improve the robustness of the algorithms

6. Deal with training issues for implementation in clinical practice (computational burden, periodical updating)

When developing challenges and algorithms, it is advisable to bear the best practices for algorithm development as recently published by Bradshaw et al. [82].

## Conclusions

The time to implement AI in medicine is now. Nuclear medicine and molecular imaging are no exception to that development. AI shows great promise to improve image quality, to personalise dosages (both in diagnosis and theranostics) and to help in image interpretation. It opens ways to fully exploit the potential of NM (which by nature is a numerical specialty), an aspect that has gathered momentum over the past decade with the advent of radiomics. As such, AI has the potential to improve clinical workflows that will increase overall efficiency but also facilitate personalised medicine for the benefit of a patient. Here, we should not forget the introduction of total body scanners which cause an enormous increase of data to be handled. It seems obvious that AI and total body scanners are natural partners to tackle this problem.

In this paper, we have highlighted the current state of the art in AI for NM. Research in AI for NM has made great strides over the past couple of years as this paper has described. We have also highlighted areas where work needs to be concentrated in order to improve overall acceptability of AI by the NM community.

In order to move forward with clinical adoption, the validation of outcomes/results of individual trials/studies is needed, and this is where multicentre and multigroup

cooperation is of the utmost importance. Comparing the performance of algorithms through challenges is needed considering both methodological and clinical outcomes. Demonstrating the potential interest of AI in NM through these challenges represents also an essential element for its adoption within our field. As with any new technology (and even more true for software related) developments, the field requires also industrial partners to be proactive in facilitating its clinical implementation.

Last but not least is education. The transmission of knowledge for the implementation of AI in NM and molecular imaging is dearly needed. These educational programs should target both the scientific and clinical aspects. Such educational programs will ensure that current and next-generation scientists and clinicians will become familiar and grow-up with AI and therefore enhance further its potential adoption and development.

**Author contribution** All the authors contributed to the study conception and design. Data collection and analysis were performed by all the authors. The first draft of the manuscript was written by Jan Pruim, and all the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

## Declaration

**Conflict of interest** DV, RH, ML and JP declare no financial interests. KBM is founder and CEO of Cercare Medical and owns shares in the company. The Ethics Committee of EANM considered the risk of biased statements of being low.

PL reports, within and outside the submitted work, grants/sponsored research agreements from Radiomics SA of which he is the founder, from ptTheragnostic/DNAmito and from Health Innovation Ventures. He received an advisor/presenter fee and/or reimbursement of travel costs/consultancy fee and/or in-kind manpower contribution from Radiomics SA, BHV, Merck, Varian, Elekta and ptTheragnostic. He has minority shares in the company Radiomics SA and MedC2. He is co-inventor of two issued patents with royalties on radiomics (PCT/NL2014/050248, PCT/NL2014/050728) licensed to Radiomics SA and one issue patent on mtDNA (PCT/EP2014/059089) licensed to ptTheragnostic/DNAmito and co-inventor of 3 non-patented invention (softwares) licensed to ptTheragnostic/DNAmito, Radiomics SA and Health Innovation Ventures plus 3 non-issues, non-licensed patents on deep learning-radiomics and LSRT (no. 17123816, PCT/NL/2020/050794, no. 2025017). He confirms that none of the above entities or funding was involved in the preparation of this paper. The Ethics Committee consider the risk of biased statements of being low.

KS received public support for research, but also from Novartis. CR reports support on amyloidosis research, partially from commercial entities. In both cases, the risk of biased statements is considered of being low.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated

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## References

1. Visvikis D, Rest CCL, Jaouen V, Hatt M. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging. 2019;46:2630–7.

2. Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE Inst Electron Eng. 2021;86:2278–324.

3. Hinton GE, Osindero S, Teh Y-W. A fast learning algorithm for deep belief nets. Neural Comput. 2006;18:1527–54.

4. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun Acm. 2017;60:84–90.

5. Torres-Velazquez M, Chen W-J, Li X, McMillan AB. Application and construction of deep learning networks in medical imaging. IEEE Trans Radiat Plasma Med Sci. 2021;5:137–59.

6. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577:89–94.

7. Haibe-Kains B, Adam GA, Hosny A, Khodakarami F, Shraddha T, Kusko R, et al. Transparency and reproducibility in artificial intelligence. Nature. 2020;586:E14–6.

8. Curtis C, Liu C, Bollerman TJ, Pianykh OS. Machine learning for predicting patient wait times and appointment delays. J Am Coll Radiol. 2018;15:1310–6.

9. Hatt M, Rest CCL, Tixier F, Badic B, Schick U, Visvikis D. Radiomics: data are also images. J Nucl Med. 2019;60:38S-44S.

10. Uribe CF, Mathotaarachchi S, Gaudet V, Smith KC, Rosa-Neto P, Bénard F, et al. Machine learning in nuclear medicine: part 1-introduction. J Nucl Med. 2019;60:451–8.

11. Zukotynski K, Gaudet V, Uribe CF, Mathotaarachchi S, Smith KC, Rosa-Neto P, et al. Machine learning in nuclear medicine: part 2-neural networks and clinical aspects. J Nucl Med. 2021;1:22–9.

12. Nensa F, Demircioglu A, Rischpler C. Artificial intelligence in nuclear medicine. J Nucl Med. 2019;60:29S-37S.

13. Reader AJ, Corda G, Mehranian A, da Costa-Luis C, Ellis S, Schnabel JA. Deep learning for PET image reconstruction. IEEE Trans Radiat Plasma Med Sci. 2021;5:1–25.

14. Lee JS. A review of deep learning-based approaches for attenuation correction in positron emission tomography. IEEE Trans Radiat Plasma Med Sci. 2021;5:160–84.

15. Sollini M, Antunovic L, Chiti A, Kirienko M. Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol. 2019;I(46):2656–72.

16. Castiglioni I, Rundo L, Codari M, Leo GD, Salvatore C, Interglanni M, et al. AI applications to medical images: From machine learning to deep learning. Phys Medica. 2021;83:9–24.

17. Currie G, Rohren E. Intelligent imaging in nuclear medicine: the principles of artificial intelligence, machine learning and deep learning. Semin Nucl Med. 2020;51:102–11.

18. European Commission. White paper: On artificial intelligence – A European approach to excellence and trust. Brussels, 19-2-2020. COM(2020) 65 final.

19. Goodman SN. A comment on replication, p-values and evidence. Stat Med. 1992;11:875–9.

20. Simmons JP, Nelson LD, Simonsohn U. False-positive psychology. Psychol Sci. 2011;22:1359–66.

21. Baker M. 1,500 scientists lift the lid on reproducibility. Nature. 2016;533:452–4.

22. Wei L, Osman S, Hatt M, ElNaqa I. Machine learning for radiomics-based multi-modality and multi-parametric modeling. Q J Nucl Med Mol Imaging. 2019;63:323–38.

23. Hatt M, Parmar C, Qi J, Naqa IE. Machine (Deep) Learning methods for image processing and radiomics. IEEE Trans Radiat Plasma Med Sci. 2019;3:104–8.

24. Häggström I, Schmidtlein CR, Campanella G, Fuchs TJ. Deep-PET: a deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Med Image Anal. 2019;54:253–62.

25. Kandarpa VSS, Bousse A, Benoit D, Visvikis D. DUG-RECON: a framework for direct image reconstruction using convolutional generative networks. IEEE Trans Radiat Plasma Med Sci. 2021;5:44–53.

26. Gong K, Guan J, Liu C-C, Qi J. PET image denoising using a deep neural network through fine tuninG. IEEE Trans Radiat Plasma Med Sci. 2019;3:153–61.

27. Mehranian A, Reader AJ. Model-based deep learning PET image reconstruction using forward-backward splitting expectation maximisation. IEEE Trans Radiat Plasma Med Sci. 2021;5:54–64.

28. Shao W, Pomper MG, Du Y. A learned reconstruction network for SPECT imaging. IEEE Trans Radiat Plasma Med Sci. 2021;5:26–34.

29. Jang H, Liu F, Zhao G, Bradshaw T, McMillan AB. Technical note: deep learning based MRAC using rapid ultrashort echo time imaging. Med Phys. 2018;45:3697–704.

30. Hwang D, Kang SK, Kim KY, Seo S, Paeng JC, Lee DS, et al. Generation of PET attenuation map for whole-body time-of-flight 18F-FDG PET/MRI using a deep neural network trained with simultaneously reconstructed activity and attenuation maps. J Nucl Med. 2019;60:1183–9.

31. Dong X, Lei Y, Wang T, Higgins K, Liu T, Curran WJ, et al. Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging. Phys Med Biol. 2020;65:055011.

32. Visvikis D, Merlin T, Bousse A, Benoit D, Laurent B. Deep learning based scatter correction for PET imaging. Eur J Nucl Med Mol Imaging. 2020;47(Suppl 1):S484.

33. Xiang H, Lim H, Fessler JA, Dewaraja YK. A deep neural network for fast and accurate scatter estimation in quantitative SPECT/CT under challenging scatter conditions. Eur J Nucl Med Mol Imaging. 2020;47:2956–67.

34. Yang J, Park D, Sohn JH, Wang ZJ, Gullberg GT, Seo Y. Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain 18 F-FDG PET. Phys Med Biol. 2019;64:075019.

35. Wang Y, Zhou L, Wang L, Yu B, Zu C, Lalush DS, et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 21st International Conference, Granada, Spain, September 16–20, 2018, Proceedings, Part I. Lect Notes Comput Sc. 2018;11070:329–37.

36. Song T-A, Chowdhury SR, Yang F, Dutta J. PET image super-resolution using generative adversarial networks. Neural Netw. 2020;125:83–91.

37. Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, et al. The first MICCAI challenge on PET tumor segmentation. Med Image Anal. 2018;44:177–95.

38. Pinochet P, Eude F, Becker S, Shah V, Sibille L, Toledano MN, et al. Evaluation of an automatic classification algorithm using convolutional neural networks in oncological positron emission tomography. Front Med. 2021;8:628179.
39. Guo Z, Li X, Huang H, Guo N, Li Q. Deep learning-based image segmentation on multimodal medical imaging. IEEE Trans Radiat Plasma Med Sci. 2019;3:162–9.

40. Wang G, Rahmim A, Gunn RN. PET parametric imaging: past, present, and future. IEEE Trans Radiat Plasma Medical Sci. 2020;4:663–75.

41. Boellaard R, Delgado-Bolton R, Oyen W, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.

42. Liang G, Fouladvand S, Zhang J, Brooks MA, Jacobs N, Chen J. GANai: Standardizing CT images using generative adversarial network with alternative improvement. BioRxiv. 2018. https://doi.org/10.1101/460188.

43. Kim DH, Wit H, Thurston M. Artificial intelligence in the diagnosis of Parkinson's disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning. Nuc Med Commun. 2018;10:887–93.

44. Choi H, Kim YK, Yoon EJ, Lee J-Y, Lee DS, Initiative ADN. Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease. Eur J Nucl Med Mol Imaging. 2020;47:403–12.

45. Son HJ, Oh JS, Oh M, Kim SJ, Lee J-H, Roh JH, et al. The clinical feasibility of deep learning-based classification of amyloid PET images in visually equivocal cases. Eur J Nucl Med Mol Imaging. 2020;47:332–41.

46. Weehaeghe DV, Devrome M, Schramm G, Vocht JD, Deckers W, Baete K, et al. Combined brain and spinal FDG PET allows differentiation between ALS and ALS mimics. Eur J Nucl Med Mol. 2020;1(47):2681–90.

47. Betancur J, Rubeaux M, Fuchs TA, Otaki Y, Arnson Y, Slipczuk L, et al. Automatic valve plane localization in myocardial perfusion SPECT/CT by machine learning: anatomic and clinical validation. J Nucl Med. 2016;58:961–7.

48. Arsanjani R, Xu Y, Hayes SW, Fish M, Lemley M, Gerlach J, et al. Comparison of fully automated computer analysis and visual scoring for detection of coronary artery disease from myocardial perfusion SPECT in a large population. J Nucl Med. 2013;54:221–8.

49. Arsanjani R, Dey D, Khachatryan T, Shalev A, Hayes SW, Fish M, et al. Prediction of revascularization after myocardial perfusion SPECT by machine learning in a large population. J Nucl Cardiol. 2015;22:877–84.

50. Hu L-H, Betancur J, Sharir T, Einstein AJ, Bokhari S, Fish MB, et al. Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry. Eur Heart J Cardiovasc Imaging. 2019;21:549–59.

51. Betancur J, Hu L-H, Commandeur F, Sharir T, Einstein AJ, Fish MB, et al. Deep learning analysis of upright-supine high-efficiency SPECT myocardial perfusion imaging for prediction of obstructive coronary artery disease: a multicenter study. J Nucl Med: Off Publ Soc Nucl Med. 2019;60:664–70.

52. Betancur J, Otaki Y, Motwani M, Fish MB, Lemley M, Dey D, et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning. JACC Cardiovasc Imaging. 2018;11:1000–9.

53. Slart RHJA, Williams MC, Juarez-Orozco LE, Rischpler C, Dweck MR, Glaudemans AWJM, et al. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT. Eur J Nucl Med Mol. 2021;1(48):1399–413.

54. Zhao Z, Pi Y, Jiang L, Xiang Y, Wei J, Yang P, et al. Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis. Sci Rep-uk. 2020;10:17046.

55. Ntakolia C, Diamantis DE, Papandrianos N, Moustakidis S, Papageorgiou EI. A lightweight convolutional neural network architecture applied for bone metastasis classification in nuclear medicine: a case study on prostate cancer patients. Healthc. 2020;8:493.

56. Wei L, ElNaqa I. AI for response evaluation with PET/CT. Semin Nucl Med. 2021;51:157–69.

57. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–62.

58. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.

59. O'Connor JPB, Aboagye EO, Adams JE, Aerts HJWL, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14:169–86.

60. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL. Machine learning methods for quantitative radiomic biomarkers. Sci Rep-uk. 2015;5:13087.

61. Deist TM, Dankers FJWM, Valdes G, Wijsman R, Hsu I, Oberije C, et al. Machine learning algorithms for outcome prediction in (chemo)radiotherapy: an empirical comparison of classifiers. Med Phys. 2018;45:3449–59.

62. Amyar A, Ruan S, Gardin I, Chatelain C, Decazes P, Modzelewski R. 3-D RPET-NET: development of a 3-D PET imaging convolutional neural network for radiomics analysis and outcome prediction. IEEE Trans Radiat Plasma Med Sci. 2019;3:225–31.

63. Hansen L, Salamon P. Neural network ensembles. IEEE Trans Pattern Anal Mach Intell. 1990;12:993–1001.

64. Hatt M, Rest CCL, Antonorsi N, Tixier F, Tankyevych O, Jaouen V, et al. Radiomics in PET/CT: current status and future AI-based evolutions. Semin Nucl Med. 2020;51:126–33.

65. Afshar P, Mohammadi A, Plataniotis KN, Oikonomou A, Benali H. From handcrafted to deep-learning-based cancer radiomics. IEEE Signal Proc Mag. 2019;36:132–60.

66. Tixier F, Jaouen V, Hognon C, Gallinato O, Colin T, Visvikis D. Evaluation of conventional and deep learning based image harmonization methods in radiomics studies. Phys Med Biol. 2021;66:245009.

67. Wang Y-R (Joyce), Baratto L, Hawk KE, Theruvath AJ, Pribnow A I, Thakor AS, et al. Artificial intelligence enables whole-body positron emission tomography scans with minimal radiation exposure. Eur J Nucl Med Mol I. 2021;48:2771–81.

68. Ljungberg M, Gleisner KS. 3-D Image-based dosimetry in radionuclide therapy. IEEE Trans Radiat Plasma Med Sci. 2018;2:527–40.

69. Flux GD, Gleisner KS, Chiesa C, Lassmann M, Chouin N, Gear J, et al. From fixed activities to personalized treatments in radio-nuclide therapy: lost in translation? Eur J Nucl Med Mol Imaging. 2018;45:152–4.

70. Zhao Y, Gafita A, Vollnberg B, Tetteh G, Haupt F, Afshar-Oromieh A, et al. Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT. Eur J Nucl Med Mol. 2020;1(47):603–13.

71. Xue S, Gafita A, Afshar-Oromieh A, Eiber M, Rominger A, Shi K. Voxel-wise prediction of post-therapy dosimetry for 177Lu-PSMA I&T therapy using deep learning. J Nucl Med. 2020;61:1424.

72. Lee MS, Hwang D, Kim JH, Lee JS. Deep-dose: a voxel dose estimation method using deep convolutional neural network for personalized internal dosimetry. Sci Rep-uk. 2019;9:10308.

73. Ataeinia B, Heidari P. Artificial intelligence and the future of diagnostic and therapeutic radiopharmaceutical development: in silico smart molecular design. Pet Clin. 2021;16:513–23.
74. Kletting P, Thieme A, Eberhardt N, Rinscheid A, D’Alessandria C, Allmann J, et al. Modeling and predicting tumor response in radioligand therapy. J Nucl Med. 2019;60:65–70.

75. Guo R, Hu X, Song H, Xu P, Xu H, Rominger A, et al. Weakly supervised deep learning for determining the prognostic value of 18F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type. Eur J Nucl Med Mol. 2021;1:1–11.

76. Shorten C, Khoshgoftaar TM. A survey on image data augmentation for deep learning. J Big Data. 2019;6:60.

77. Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045–57.

78. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neurology. 2010;74:201–9.

79. Quellec G, Charrière K, Boudi Y, Cochener B, Lamard M. Deep image mining for diabetic retinopathy screening. Med Image Anal. 2017;39:178–93.

80. Brocki L, Chung NC. Concept saliency maps to visualize relevant features in deep generative models. Arxiv [Internet]. 2019;1910.13140. https://doi.org/10.48550/arXiv/1910.13140.

81. O’Neil C. Weapons of math destruction. How big data increases inequality and threatens democracy. New York: Crown Publishing Group; 2016.

82. Bradshaw TJ, Boellaard R, Dutta J, Jha AK, Jacobs P, Li Q, et al. Nuclear medicine and artificial intelligence: best practices for algorithm development. J Nucl Med. 2022;63:500–10.

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