Patent Description:
Parasitic infections affect billions of people globally, resulting in massive socioeconomic burden. Although usually associated with low-income countries, parasitic infections are also becoming an increasing health concern in developed countries. In the United States alone, millions of people are affected by various parasites, which can lead to severe illnesses and even death. Motility is common among disease-causing organisms, from unicellular pathogenic bacteria and parasitic protozoa to multicellular parasitic worms and ectoparasites. Motility is the ability of a cell or organism to move of its own accord using its own energy. The ability of an organism to move itself from one location to another has obvious benefits for successful infection and transmission, and motility is often central to virulence. Despite the importance of motility for a parasitic lifestyle, parasite motility remains an understudied area of research and motility-based diagnostics are largely underexplored.

Human African trypanosomiasis (HAT), also known as sleeping sickness, and Chagas disease (i.e., American trypanosomiasis) are examples of neglected tropical diseases (NTDs) caused by motile protozoan parasites. As NTDs, they have historically been given little attention, disproportionately affect the world's poorest people, and lack adequate medical interventions for diagnosis and treatment. There are no vaccines, and existing chemotherapeutics suffer from high toxicity and drug resistance. These two devastating diseases, HAT and Chagas disease, are caused by related trypanosome parasites. Trypanosoma brucei (T. brucei gambiense and T. brucei rhodesiense subspecies) is responsible for HAT, and related species cause animal diseases that present a substantial economic burden in some of the poorest areas of the world. The parasite is transmitted to humans by the tsetse fly and survives extracellularly in blood and tissues, with dramatic impacts on the central nervous system (CNS). HAT is endemic in ~<NUM> sub-Saharan Africa countries with ~<NUM> million people at risk of infection. The number of reported cases has dropped to historic lows, but past declines in case numbers have been followed by major epidemics. Therefore, HAT remains an important human health risk. Chagas disease, on the other hand, is caused by Trypanosoma cruzi (T. cruzi), which invades and replicates inside host cells causing severe pathology within host tissues. Chagas disease is mostly transmitted by the bite of triatomine bugs, but other transmission routes include blood transfusion and ingestion of contaminated food or drink. The disease is endemic in Latin America where it affects over <NUM> million people. It is estimated that more than <NUM>,<NUM> people are infected in the United States with further increases expected as globalization and climate change impact the distribution of disease-transmitting vectors.

Both trypanosomiases can be classified into an initial stage during which trypanosomes circulate in the bloodstream and medical treatment is most effective (stage I HAT and acute Chagas disease), and a later stage that is exceedingly more difficult, if not impossible, to cure (stage II HAT and chronic Chagas disease). Therefore, early detection is crucial for both diseases. However, rapid and sensitive diagnosis remains challenging, particularly in resource-limited settings. In the diagnosis of HAT, it is also essential to assess the stage of the disease to determine the appropriate therapeutic strategy. While trypanosomes remain in the blood and lymph in stage I HAT, stage II HAT is characterized by trypanosomes crossing the blood-brain barrier and invading the central nervous system (CNS), causing neurological symptoms and eventually death if untreated. Because the drugs used to treat stage I and stage II HAT are not interchangeable, and drugs for stage II may be more toxic, it is very important to identify the stage of the disease to inform the selection of treatment regimen. Stage determination is currently done by collecting cerebrospinal fluid (CSF) via a lumbar puncture and examining the CSF under a microscope for presence of white blood cells (WBCs) and trypanosomes.

Both trypanosome species are typically ~<NUM> in length and ~<NUM> in width and use flagellum-mediated motility for parasite propulsion. The detection of these motile parasites in large volume bodily fluids such as blood and CSF are an important clinical challenge. For decades, the standard screening test for T. gambiense HAT has been the card agglutination test for trypanosomiasis (CATT), which detects the presence of antibodies against a specific parasite antigen. However, CATT suffers from practical limitations as well as low specificity and sensitivity in some areas. Moreover, a positive CATT test must typically be confirmed with direct visual observation in blood samples. Several molecular and immunological detection methods have been developed including polymerase chain reaction (PCR) and rapid diagnostic tests (RDTs), but these methods are limited by insufficient specificity or sensitivity, the need for sophisticated equipment and highly trained personnel, or high production costs. Thus, microscopic evaluation is still widely used for primary or secondary diagnosis, and direct observation of CSF remains the sole method for HAT stage determination. Each milliliter of whole blood typically contains billions of red blood cells (RBCs), millions of white blood cells (WBCs) and hundreds of millions of platelets. In contrast, blood parasitemia fluctuates during the course of infection and often is below <NUM> parasites/mL, making microscopic identification of trypanosomes a needle-in-a-haystack problem. The low sensitivity of direct observation methods therefore requires analytical separation devices such as centrifugation or ion exchange purification, which partially limit analysis in resource-limited settings. Thus, there is still a major need for development of new methods with high sensitivity and throughput that can reduce costs and simplify diagnosis.

<CIT> discloses an imaging platform for the detection of motile objects in a fluid sample comprising one substantially optically transparent sample holder; two coherent light sources and one corresponding image sensor associated with the two coherent light sources; a computing device configured to receive time-varying holographic speckle pattern image sequences obtained by the image sensor, the computing device comprising computational motion analysis software configured to generate a three-dimensional (3D) contrast map of motile objects within the optically transparent sample holders.

<CIT> discloses a detection apparatus having a read head including a plurality of microfluorometers positioned to simultaneously acquire a plurality of the wide-field images in a common plane; and (b) a translation stage configured to move the read head along a substrate that is in the common plane.

<NPL>), relates to a fully automated tracking method for videos of fluid samples, using a convolutional neural network for particle localization from image data, comprising over <NUM>,<NUM> parameters, and using machine learning techniques to train the network on a diverse portfolio of video conditions.

<CIT> relates to systems and methods for determining the presence of an analyte utilize a plurality of images of a sample slide including multiple fields-of-view having multiple focal planes therein.

<CIT> relates to an analysis system including a fluid analyzer configured to monitor at least one characteristic of the fluid sample to be analyzed; and an inclined rail; wherein the stage is configured to move along the inclined rail to cause the sample holder to move with a first component of motion along an analysis axis of the fluid analyzer and simultaneously with a second component of motion orthogonal to the analysis axis of the fluid analyzer, wherein the first component of motion affects a focus of the fluid analyzer relative to at least one constituent of the fluid sample to be analyzed.

<CIT> relates to lens-free imaging of a sample or objects within the sample uses multi-height iterative phase retrieval and rotational field transformations to perform wide FOV imaging of pathology samples with clinically comparable image quality to a benchtop lens-based microscope.

To address this important challenge, the present invention is directed to an imaging platform for detection of motile objects of interest in a fluid sample, according to claim <NUM>.

Another embodiment of the present invention is directed to a method of using the imaging platform, according to claim <NUM>.

An exemplary imaging platform according to the present invention was constructed and configured to increase the throughput and reduce the limit of detection (LoD) for rapid screening of large fluid volumes (~<NUM> or larger). In this example, the imaging platform includes three identical lensless speckle imaging modules mounted on a translation stage to screen three individual sample tubes in parallel. Each imaging module is translated to different sections of the capillary tube containing the liquid sample, where the CMOS image sensor captures high-frame-rate video sequences before moving on to the next section. Using this approach, ~<NUM>, or more, of fluid sample may be prepared, screened and analyzed, all within ~<NUM> minutes, using the exemplary imaging platform. Compared to standard benchtop optical microscopes, this imaging platform design provides orders of magnitude increase in the screened sample volume (which is very important for the detection of parasites at low concentrations). In addition, the imaging platform may be significantly more compact and lightweight (e.g., weighing about <NUM> or less). Furthermore, since a relatively large sample volume is screened computationally in the axial direction, the imaging device does not need high precision in its opto-mechanical design, which also makes the platform highly cost-effective, where its parts may cost about $<NUM>,<NUM> or less in total even under very low volume manufacturing.

The exemplary imaging platform was tested using trypanosomes to test the mobile platform and it demonstrated its capability to detect parasites in spiked whole blood and CSF samples, which are important for the diagnosis and stage determination of HAT as well as the diagnosis of acute Chagas disease. The spiking experiments were performed at a series of concentrations using T. brucei (a non-human infectious subspecies of Trypanosoma) as a model parasite for Tb. gambiense, Tb. rhodesiense and T. Through deep learning-based classification, it was shown that as low as <NUM> parasites per mL of whole blood and <NUM> parasites per mL of CSF can be reliably detected using the imaging platform. Furthermore, the success of the platform to detect other motile parasites in bodily fluids by imaging Trichomonas vaginalis (T. vaginalis), the protozoan parasite responsible for trichomoniasis, which is the most common, non-viral sexually transmitted disease (STD) affecting <NUM> million people in the United States and <NUM> million worldwide, was demonstrated. Accordingly, the label-free, motility-based parasite detection platform can provide a cost-effective and portable approach for rapid and sensitive screening of trypanosomes and other motile parasites in resource-limited settings, or as a high-throughput analytical research tool to study motile organisms in 3D. While the exemplary imaging platform described herein was used to detect parasites, it should be understood that the platform may be used in the detection of other motile species that are not parasites. For example, this includes sperm and other multicellular or single cellular species that are motile.

The foregoing and other aspects of embodiments are described in further detail with reference to the accompanying drawings, wherein like reference numerals refer to like elements and the description for like elements shall be applicable for all described embodiments wherever relevant. Reference numerals having the same reference number and different letters (e.g., 104a, 104b, 104c) refer to like elements and the use of the number without the letter in the Detailed Description refers to each of the like elements.

The present invention is directed to an imaging platform for label-free detection of motile objects <NUM> in a sample (see <FIG>). <FIG> illustrate an imaging platform <NUM> for performing lensless holographic time-resolved speckle imaging of a sample <NUM>, according to one embodiment of the present invention. The imaging platform <NUM> is made up of five main modules: (<NUM>) a scanning head <NUM> having one or more lensless holographic speckle imagers <NUM> (the illustrated embodiment of an imaging platform <NUM> shown in <FIG> and <FIG> and described herein has three imagers 104a, 104b, 104c), (<NUM>) a translation stage <NUM>, (<NUM>) a main housing <NUM> that holds the components of the imaging platform <NUM>, (<NUM>) electronic circuitry <NUM> configured to control and automate various functions of the imaging platform <NUM>, and (<NUM>) a computing device <NUM> having a control program <NUM> which provides a graphical user interface (GUI) <NUM>. The GUI <NUM> is configured to enable a user to initiate the screening of a current sample in addition to various other functionalities, such as customizing image acquisition parameters, performing a live view of the diffraction patterns, taking a snapshot, querying results, sample tracking, generating reports and outputs, and stopping the acquisition.

The scanning head <NUM> includes one or more lensless imagers 104a, 104b, 104c housed within a scanning head housing <NUM> (e.g., printed by 3D-printed plastic, molded plastic, formed metal, etc.). Each lensless imager 104a, 104b, 104c includes an illumination source <NUM>. The illumination source <NUM> may be a laser diode, such as a <NUM>-nm laser diode (product no. AML-N056-<NUM>-<NUM>, Arima Lasers Corp. , Taoyuan, Taiwan) having an output power of ~<NUM> mW, or other suitable illumination device. For instance, the illumination source <NUM> other than a laser diode, including a light-emitting diode (LED), another laser light source, and the like.

The emitted light <NUM> from the illumination source <NUM> passes through an optional aperture <NUM>. The aperture <NUM> may be a 3D-printed aperture or other suitably constructed aperture (e.g., molded, machined, etc.). The aperture <NUM> functions to limit the emission angle of the emitted light and avoid light leakage into the adjacent imagers <NUM>. The aperture <NUM> is optional and may not be present in all embodiments. The aperture <NUM> serves to prevent light leakage to the nearby image sensor <NUM>. In embodiments where the light leakage is not an issue (e.g., where spacing or configuration of lensless imagers <NUM> does not suffer from light leakage), the aperture may be omitted.

The sample <NUM> is loaded into substantially optically transparent fluidic holders 120a 120b, 120c (also referred to as "sample holders"). The term "substantially optically transparent" means that the element is sufficiently transparent to obtain images <NUM> of a sample <NUM> through the element of sufficient quality to identify motile objects <NUM> in the sample <NUM>. In one embodiment, each fluidic holder 120a, 120b, 120c is a glass capillary tube. The capillary tube may be rectangular in cross-sectional profile, or other suitable cross-sectional profile, such as circular, oval, etc.). The fluidic holder <NUM> is filled with the sample <NUM> (e.g., a bodily fluid to be screened), and is positioned a z<NUM> distance <NUM> below the illumination source <NUM>. In the illustrated embodiment, the z<NUM> distance <NUM> is ~<NUM> below the illumination source <NUM>. Again, the aperture <NUM> is optional and may not be present in all embodiments. The aperture <NUM> serves to prevent light leakage to the nearby image sensor <NUM> of the adjacent imagers <NUM>. In embodiments where the light leakage is not an issue (e.g., where spacing or configuration of the lensless imagers <NUM> does not suffer from light leakage), the aperture <NUM> may be omitted.

Each of the imagers 104a, 104b, 104c has an image sensor <NUM> positioned on the opposing side of the respective fluidic holder <NUM> from the respective illumination source <NUM> such that it can image a diffraction or speckle pattern of the emitted light <NUM> from the illumination source <NUM> through the sample <NUM> at a section of the sample <NUM> based on the position of the scanning head <NUM>. For example, in the illustrated embodiment, the image sensor <NUM> is positioned below the fluidic holder <NUM>, with the illumination source <NUM> above the fluidic holder <NUM>. The image sensor <NUM> may be any suitable image sensor, such as a <NUM>-megapixel CMOS image sensor (product no. acA3800-<NUM>, Basler, Ahrensburg, Germany) with a <NUM> pixel size and an active area of <NUM> × <NUM> (<NUM><NUM>). The image sensor <NUM> is positioned below the illumination source <NUM> a z<NUM> distance <NUM>. The z<NUM> distance <NUM> is typically much greater than the z<NUM> distance. In the illustrated embodiment, the z<NUM> distance <NUM> (i.e., the air gap) between the image sensor <NUM> and the bottom surface of the fluidic holder <NUM> is about <NUM>-<NUM>, or <NUM>-<NUM>, or <NUM>-<NUM>, to reduce the heat transfer from the image sensor <NUM> to the sample <NUM>. <FIG> illustrates that the z<NUM> distance <NUM> may be about <NUM>, in some embodiments.

Because each image sensor <NUM> has one or more circuit boards <NUM> that generate heat, heat sinks <NUM> are optionally inserted between the circuit boards <NUM> and arranged on the sides of the scanning head <NUM> to dissipate heat and prevent image sensor <NUM> malfunction and/or damage. The heat sinks <NUM> may be custom-made aluminum heat sinks, or other suitable heat sinks, including other materials and construction.

The embodiment used in the Examples described herein uses a scanning head <NUM> with three identical lensless imagers 104a, 104b, 104c that image three different capillary tubes 120a, 120b, 120c. These tubes 120a, 120b, 120c could be loaded with samples from different patients or the same patient. It should be understood that more (or fewer) lensless imagers <NUM> may also be used.

The translation stage <NUM> is configured to move the scanning head <NUM> in order to move the imagers <NUM> relative to the fluidic holders <NUM> so that the imagers <NUM> can obtain images <NUM> of different regions of the sample <NUM> contained in the respective fluid holders <NUM>. In the illustrated embodiment, the translation stage <NUM> moves the scanning head <NUM> in a linear direction along the length of the fluidic holders <NUM> and is thus referred to as a linear translation stage <NUM>. In the illustrated embodiment, the linear translation stage <NUM> includes two linear motion shafts 130a, 130b which are mounted to the aligned parallel to the longitudinal axis of the fluidic holders <NUM>. The motion shafts 130a, 130b may be product no. <NUM>, Makeblock Co. , Shenzhen, China, or other suitable motion shafts. The linear translation stage also has two linear motion sliders <NUM> which are coupled, and controllably moveable relative, to the motion shafts 130a, 130b. The linear motion sliders <NUM> may be product no. <NUM>, Makeblock Co. , Shenzhen, China. The linear translation stage <NUM> also includes a timing belt <NUM> (e.g., product no. B375-210XL, ServoCity, Winfield, KS, or other suitable timing belt) operably coupled to two timing pulleys 136a, 136b (e.g., product no. <NUM>, ServoCity, Winfield, KS, or other suitable timing pulley) and a stepper motor <NUM> (e.g., product no. <NUM>, Adafruit Industries LLC. , New York City, NY, or other suitable motor) operably coupled to the timing belt <NUM>.

The scanning head <NUM> is mounted onto the motion sliders <NUM> using screws or other suitable fasteners. The scanning head <NUM> with the attached motion sliders <NUM> moves along the stationary linear motion shafts 130a, 130b. The stepper motor <NUM> provides power to drive the coupled timing belt <NUM> and timing pulleys <NUM> to move the scanning head <NUM> back-and-forth along the linear motion shafts 130a, 130b. While the specific linear translation stage <NUM> utilized and disclosed herein may be used with the imaging platform <NUM>, it should be understood that other translation mechanisms and devices that are configured to move the scanning head <NUM> in a linear direction relative to the fluidic holders <NUM> may be used. These may include motor or servo-based devices that are mechanically coupled or linked to the scanning head <NUM> to impart linear movement. Likewise, the translation stage <NUM> may translate in different directions depending on the sample volume that is to be scanned. For example, a three-dimensional volume may be scanned in orthogonal (or other directions) to cover the sample volume. Thus, a variety of different translation motions may be used in conjunction with the translation stage <NUM>.

The computing device <NUM> is configured to control the operation of the imaging platform <NUM>. In the illustrated embodiment, the computing device <NUM> is a laptop computer, but the computing device <NUM> may include other computer-based devices (e.g., a personal computer or in some instances a tablet computer or other portable computing device). The computing device <NUM> may include one or more microprocessors <NUM>, a storage device <NUM>, a graphics processing unit (GPU) <NUM>, and a display <NUM>.

Referring to the schematic diagram of <FIG>, the electronic circuitry <NUM> includes a printed circuit board (PCB) <NUM> configured to automate the imaging platform <NUM>. The PCB <NUM> includes a microcontroller <NUM> (e.g., a Teensy LC, PJRC) operably connected to the computing device <NUM> via a USB <NUM> interface (or other suitable interface). The microcontroller <NUM> also includes illumination driver circuits <NUM> (e.g., laser diode driver circuits or other suitable driver circuits), and a stepper motor driver circuit <NUM>. The illumination driver circuits <NUM> may be built from constant current circuits. For instance, in the case of laser diode driver circuits, illumination driver circuits <NUM> may be built from product no. LM317DCYR, Texas Instruments. The stepper motor driver circuit <NUM> may be product no. TB6612, Adafruit, or other suitable stepper motor driver circuit.

In the illustrated embodiment, the illumination source <NUM> (e.g., laser diodes) and the stepper motor <NUM> are powered using a <NUM> V power adapter <NUM>. Various digital switches 156a, 156b, 156c built from metal-oxide-semiconductor field-effect transistors (MOSFETs) are controlled by the digital outputs from the microcontroller <NUM> to cut the power to the laser diodes <NUM> and the image sensors <NUM> when they are unused. Specifically, to control the power to the image sensors <NUM>, including cutting the power to the image sensor <NUM>, the power wire of a USB <NUM> cable of the image sensor <NUM> is cut and a MOSFET-based digital switch 156a is inserted into the power line.

The computing device <NUM> contains a control program <NUM> that is used to control and interact with data obtained from the imaging platform <NUM>. For example, in the specific embodiment disclosed herein, the control program <NUM> is a Windows®-based application written in C-Sharp programming language (C#). The control program <NUM> includes a GUI <NUM> which enables the user to initiate the screening of the current sample <NUM>, in addition to various other functionalities, such as customizing image acquisition parameters, performing a live view of the diffraction patterns, taking a snapshot, and stopping the acquisition. It should be appreciated that other programming languages or scripts may be used as well.

Accordingly, the control program <NUM> controls the imaging platform <NUM> to obtain the time-varying holographic speckle pattern image sequences. After the sample <NUM> is loaded into the fluidic holders 120a, 120b, 120c on the imaging platform <NUM>, and the sample <NUM> is allowed to settle for a predetermined waiting time (e.g., a waiting time of <NUM>-<NUM> minutes, for instance, <NUM> minutes for lysed whole blood and <NUM> minutes for artificial CSF, see <FIG> for details), the user presses a "record" button on the GUI <NUM> to start acquisition. The control program <NUM> is configured to program the imaging platform <NUM> to scan the fluidic holders 120a, 120b, 120c at a predetermined number of discrete positions to image different regions of the sample <NUM> within the fluidic holders 120a, 120b, 120c. For instance, in the illustrated embodiment, the imaging platform <NUM> may be programmed to scan the fluidic holders 120a, 120b, 120c at <NUM> discrete positions, with a distance of ~<NUM> between spatially adjacent ones. This results in a total screening volume of <NUM> (discrete scanning positions) × <NUM><NUM> (FOV of the image sensor <NUM>) × <NUM> (channel height of the capillary tube <NUM>) ≈ <NUM> per lensless speckle imager <NUM>, and ~<NUM> for the three parallel imagers combined. At each of the <NUM> positions, to achieve a high frame rate (~<NUM> fps), the FOV of the image sensor <NUM> is split into two halves (i.e., the upper <NUM> rows and the lower <NUM> rows of the pixels), with each half capturing <NUM> frames (for lysed blood) or <NUM> frames (for CSF) sequentially (see <FIG>).

The temperature of the image sensor <NUM> rises when it is powered, leading to temperature gradient-induced convection flow of the liquid sample <NUM>. An example of a temperature gradient-induced convection flow for the exemplary imaging platform <NUM> is illustrated in <FIG>. To mitigate these problems, two measures may be taken. First, instead of scanning the <NUM> positions unidirectionally, the imaging platform <NUM> may be configured to scan in a back-and-forth fashion. For example, assume the <NUM> positions are represented by positions #<NUM>, #<NUM>,. , #<NUM>, which are spatially ordered. Instead of scanning in the order of #<NUM>, #<NUM>,. , #<NUM>, the control program <NUM> programs the imaging platform <NUM> to scan with a larger step size of <NUM> positions, and whenever the scanning head <NUM> cannot move forward with this step size (because a step of <NUM> positions moves past the end positions #<NUM> or #<NUM>), the scanning head <NUM> is moved to the unscanned position with the smallest position number. In other words, in this example, the imaging platform <NUM> first scans positions #<NUM>, #<NUM>, #<NUM>, and #<NUM>. Then, the scanning head <NUM> is moved back to position #<NUM>, followed by #<NUM>, #<NUM>, and #<NUM>, and so on. This scanning pattern largely prevents heat accumulation at a given section of the capillary tube <NUM>, which has sufficient time to cool down before the scanning head <NUM> comes back to its vicinity. As a second measure, a predetermined "downtime" (e.g., a six (<NUM>) second "downtime", or a "downtime" from <NUM>-<NUM> seconds) may be added between scanning positions to allow the image sensor <NUM> to cool down. After completing the acquisition at a given position, the power to the image sensor <NUM> is cut by a MOSFET-based digital switch 156a added into the USB <NUM> cable. After the predetermined wait time (e.g., six seconds), the stepper motor <NUM> moves the scanning head <NUM> to the next position, where the power to the image sensor <NUM> is restored to capture the next set of images <NUM>.

The acquired sequence of images <NUM> (movies) are saved to the storage device <NUM> (e.g., a hard drive) for processing. All three image sensors <NUM>, capturing uncompressed <NUM>-bit images <NUM>, generate a total data rate of ~<NUM> MB/s, which slightly exceeds the average write-speed of a typical storage device <NUM> (see <FIG>), such as a hard drive (e.g., a solid-state hard drive (SSD)). Therefore, a queue is created in the random-access memory <NUM> (RAM) for each image sensor <NUM> to temporarily buffer the incoming image data, and another thread is created to constantly move the image data from the buffer into the storage device <NUM> (e.g., an SSD). However, because all the remaining image data can be fully saved to the storage device <NUM> (e.g., an SSD) during the aforementioned downtime between positions, the total image acquisition time per test is not increased due to the limited write-speed. As a more time-efficient alternative, the acquired images <NUM> can be temporarily stored in the RAM, while they are constantly moved to the GPUs for processing in batches corresponding to each image sequence. In this way, the image processing can be performed concurrently with the image acquisition, reducing the total time per test.

A CMA algorithm <NUM> (e.g., programmed into CMA software <NUM>) is utilized to generate 3D contrast data from particle locomotion in noisy holograms and speckled interference patterns and also applies deep learning-based classification to identify the signals corresponding to the parasite of interest. As an example, <FIG> depict an exemplary process (i.e., CMA algorithm and deep learning-based classification method) used to detect trypanosomes from lysed whole blood, whereas in other application settings (e.g., trypanosome detection in CSF), minor changes to the procedure may be applied, as explained below. The CMA algorithm <NUM> is configured to take the raw holographic diffraction patterns acquired by each image sensor <NUM> at each scanning position as input. The CMA software <NUM> may be used to count the number of motile species in the sample <NUM> which can then be used to calculate the concentration of the species of the sample <NUM> (given the known volume of sample). The CMA software <NUM> may also classify a particular sample as positive (+) or negative (-) based on the count or concentration of motile species. For example, threshold cutoff values may be used to demarcate between a positive or negative sample. The analysis results may be presented to the user on the GUI <NUM>. In addition, movie(s) of the movement of the motile objects <NUM> may be viewed using the GUI <NUM>. As noted herein, while the imaging platform <NUM> is particularly suited for the detection of parasites it may also be used for other motile species (e.g., sperm or other biological motile species). The samples <NUM> may include biological samples but may also include environmental samples in other embodiments.

To sustain a high frame rate (~<NUM> fps) which is essential to the parasite detection technique, the full field of view (FOV) of each of the image sensors <NUM> was split in two halves, each ~<NUM><NUM>. <FIG> show the raw speckle patterns of a lysed, trypanosome-spiked whole blood sample captured by the image sensor <NUM>. Even though this simple lysis process has significantly reduced the density of the blood sample, the interference patterns (e.g., see <FIG>) are still highly dense due to the random light scattering resulting from the cell debris in the lysed blood. As a result, the diffraction patterns of the optically transparent and weakly scattering trypanosomes (see <FIG>, shown by arrows) are buried under the speckle patterns, making their direct detection extremely challenging.

To address this challenge, the spatial-temporal variations in the detected speckle patterns due to the rapid locomotion of motile trypanosomes within blood can be utilized. A CMA algorithm <NUM> (or CMA software <NUM>) taking advantage of this was developed, which involves holographic back-propagation, differential imaging (with an optimally-adjusted frame interval for trypanosome locomotion), and temporal averaging, conducted at each horizontal cross section within the sample volume. Object function normalization (OFN) was introduced into each differential imaging step to suppress potential false positives due to unwanted, strongly scattering objects within the sample. The algorithm was then followed by post-image processing and deep learning-based classification to identify the signals caused by trypanosomes (see the description below for details). <FIG> exemplify the result of this computational process, where the "hotspots" in the processed images <NUM> correspond to motile trypanosomes. To better illustrate this, based on the calculated 3D locations of the three hotspots in <FIG> (indicated by white arrows), in-focus movies were created of the amplitude and phase channels of the back-propagated diffraction patterns. Rapid locomotion of these trypanosomes can be observed in this video, although partially obscured by the interference patterns created by the other non-motile objects (e.g., cell debris) in the sample.

Similarly, the results of imaging trypanosomes within WBC-spiked artificial CSF samples are shown in <FIG>. Because CSF is mostly a clear medium, the background noise level caused by quasi-static scatterers in the medium is significantly lower compared to the motile trypanosome signal level (i.e., the hotspots in <FIG>). Digitally focused amplitude and phase movies also show lower-noise reconstructions of these motile trypanosomes.

As detailed in Table <NUM> below, ><NUM>% of the total image processing time to image and detect these trypanosomes is spent on the CMA algorithm <NUM>, which involves thousands of fast Fourier transforms of ~<NUM>-megapixel images <NUM> for each recorded image sequence (see the Methods section below for details). Therefore, graphics processing unit (GPU) <NUM> based parallel computing is helpful for the speed-up of the CMA algorithm <NUM>. Using a single GPU <NUM>, the entire image processing task for one experiment (<NUM> image sequences in total for the three parallel image sensors <NUM>) takes ~<NUM> minutes and ~<NUM> minutes for blood and CSF samples, respectively. When using two GPUs <NUM>, because each GPU <NUM> is given a separate image sequence to process at a given time, there is minimal interference between the GPUs <NUM> and maximal parallelism can be achieved. Therefore, ~<NUM>-fold speed-up is observed when using two GPUs <NUM>, resulting in a total image processing time of ~<NUM> minutes and ~<NUM> minutes for blood and CSF experiments, respectively. Combined with all the other sample preparation steps, the total detection time per test amounts to ~<NUM> minutes and ~<NUM> minutes for blood and CSF samples, respectively (see <FIG> for details).

The LoD of the exemplary imaging platform <NUM> was determined for detecting trypanosomes in lysed whole blood by performing serial dilution experiments, and the results are shown in <FIG>. In these experiments, trypanosome-infected mouse blood was infected into uninfected blood to generate a series of parasite concentrations, including <NUM>-<NUM> (negative control), <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, <NUM>,<NUM>-<NUM> and <NUM>,<NUM>-<NUM>, where N = <NUM> replicate experiments were performed at each concentration. As shown in <FIG>, no false positives were found in the three negative control samples, while for the three <NUM>-<NUM> experiments, the detected concentration was <NUM> ± <NUM>-<NUM>. Therefore, it can be concluded that the LoD is ~<NUM> trypanosomes per mL of whole blood, which is <NUM>× better than the best parasitological detection method currently available (i.e., the mini anion exchange centrifugation technique, mAECT, see table in <FIG>). <FIG> also reveal that the recovery rate (detected trypanosome concentration divided by the spiked concentration) of the technique ranges from ~<NUM>%-<NUM>% (at the lower end of the tested concentrations) to ~<NUM>%-<NUM>% (at the higher end). This concentration-dependent recovery rate is possibly related to the proximity of trypanosomes to each other at higher concentrations, which results in more than one trypanosome in a <NUM>×<NUM>-pixel cropped image that might be misclassified as negative by the deep learning-based classifier (see the discussion below for details), leading to underestimation of the true number of trypanosomes in the sample. This drop in the recovery rate observed at high concentrations can be potentially compensated through calibration.

brucei, stage determination is critical for determining the most appropriate treatment regimen. This is currently done by collecting CSF via a lumbar puncture and examining the CSF under a microscope. Patients with ≤<NUM>µL-<NUM> WBCs and no trypanosomes in the CSF are classified as stage I; otherwise, if there are ><NUM>µL-<NUM> WBCs or if trypanosomes are found in the CSF, they are classified as stage II. To address this need for high-throughput CSF screening, the LoD of the exemplary imaging platform <NUM> to detect trypanosomes in CSF was also quantified. For this purpose, an artificial CSF sample that is spiked with human WBCs was used, where cultured trypanosomes were spiked into the artificial CSF solution at concentrations of <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-<NUM>, in addition to a negative control (N = <NUM> for each concentration). The concentration of spiked human WBCs was selected as <NUM> WBCs/µL to evaluate the performance of the device to detect trypanosomes in a scenario where the WBC concentration was four times higher than the <NUM>µ. L-<NUM> threshold used in stage determination. Unlike the blood sample, the CSF solution is optically clear and lysis was not needed, which helped us further improve the LoD: as shown in <FIG>, the exemplary imaging platform <NUM> was able to detect trypanosomes spiked in CSF with a very low LoD of <NUM> trypanosomes per mL.

Although the parasite T. brucei was chosen to validate the motility-based detection approach of the imaging platform <NUM>, it is understood that this approach is broadly applicable for the detection of a variety of motile microorganisms. As a preliminary test of the performance of the exemplary imaging platform <NUM> on a completely different motile parasite, T. vaginalis was selected. vaginalis is the protozoan parasite responsible for trichomoniasis, which is the most common non-viral STD in the United States and worldwide. T vaginalis infects the urogenital tract of both women and men. Although often asymptomatic, T. vaginalis infection has been associated with increased risk related to other health conditions including human immunodeficiency virus (HIV) infection, pre-term labor, pelvic inflammatory disease and prostate cancer. For the diagnosis of trichomoniasis, cell culture followed by microscopy remains the best, most reliable method, as it is highly sensitive and can detect T. vaginalis from an inoculum containing as few as three parasites per mL. However, it is limited by the high cost, inconvenience, a long examination time, as well as susceptibility to sample contamination. The most common diagnostic method, wet-mount microscopy, suffers from poor sensitivity (<NUM>%-<NUM>%). Thus, the highly sensitive lensless time-resolved holographic speckle imaging method could be of substantial benefit.

With only minor adjustments to the CMA algorithm <NUM> (see the discussion below), it was demonstrated that the exemplary imaging platform <NUM> can detect T. vaginalis in phosphate-buffered saline (PBS) solution and culture medium (see <FIG>, <FIG>). Based on these experiments, is can be seen that T. vaginalis creates significantly stronger signal intensities compared to trypanosomes in CSF (see <FIG>), which is related to the intense locomotion and strong light scattering of T. This suggests that the platform can potentially be used to achieve a similar, if not better, sensitivity level for T. vaginalis, e.g., reaching ≤ <NUM> parasites per mL. More testing may be needed to establish the LoD of T. vaginalis from different environments such as culture medium and urine, corresponding to different clinical needs, such as the detection of T. vaginalis from diluted vaginal secretion or directly from urine.

A new imaging platform <NUM> and methods for motility-based parasite detection has been presented, based on lensless time-resolved holographic speckle imaging. The new imaging platform <NUM> has been demonstrated as being effective for rapid detection of trypanosomes within lysed blood and CSF, achieving an LoD that is better than the current parasitological methods (see <FIG> and <FIG>). This automated technique has the potential to improve parasite screening efficiency, while reducing the need for highly specialized and expensive equipment and expertise that are essential to PCR-based or microscopic detection methods. The total cost for all the parts of the exemplary imaging platform <NUM> used in the Examples, excluding the laptop <NUM>, is less than $<NUM>; and this cost can be easily reduced to $<NUM>-<NUM> under large volume manufacturing. The total analysis time, including all the sample preparation steps, is only ~<NUM>, which is comparable to or faster than most existing methods (see <FIG>). This motility-based method achieves high sensitivity without requiring specialized detection reagents, refrigeration, centrifugation or purification, making it more versatile for the analysis of different types of samples (e.g., blood, CSF) and is less susceptible to differences between parasite sub-species or isolates from different regions of the world. Therefore, the presented prototype could be readily adapted to any established or mobile clinic with access to electricity or battery power, representing an advancement that could be a useful addition to existing diagnostic methods.

This diagnostic method could also be beneficial for improving the diagnosis of bloodstream HAT or Chagas infection, or facilitating earlier identification of stage II HAT cases, when the parasitemia in the CSF is under the LoD of traditional methods and when the WBCs in the CSF are still scarce. The imaging platform <NUM> may also be useful for follow-up after disease treatment in order to screen patients for earlier and more sensitive detection of relapse. These advances could result in improved treatment outcomes for patients and increase the cure rate of disease. In addition to HAT, animal trypanosomiasis severely limits economic development. Therefore, applying motility-based detection to aid screening of infected livestock and development of vector control options could help to raise endemic areas out of poverty. In the case of Chagas disease, this technique could be adapted for screening of blood donors or blood products as well as sugarcane juice and acai juice products to help reduce various routes of transmission. Given the large populations at risk, the ability to rapidly analyze various types of samples/liquids in a simple and automated fashion will be particularly critical for developing a viable strategy to screen samples in regions where disease incidence declines owing to eradication efforts.

The imaging platform <NUM> and label-free detection method take advantage of the locomotion patterns of parasites to maximize the detection signal-to-noise ratio (SNR). Trypanosomes are known for their incessant motion, and motility is crucial to their survival as well as their virulence in the host. The swimming behavior of trypanosomes is highly complex. Because the flagellum is laterally attached to the cell body, parasite translocation is accompanied by cell body rotation, resulting in a "corkscrew" swimming pattern. Moreover, in addition to cell translocation, the flagellum generates rapid, three-dimensional beating patterns. The average beating frequency of T. brucei is estimated as <NUM>±<NUM> in forward moving cells and <NUM>±<NUM> in backward moving ones, whereas the rotational frequency of forward moving cells is <NUM>±<NUM>. The frame rate that matches the average beating frequency (forward moving), according to the Nyquist sampling rate, is equal to <NUM> fps. In other words, a frame rate of at least <NUM> fps is able to record the speckle changes corresponding to each flagellar stroke; and even higher frame rates can record the speckle changes with finer time resolution, corresponding to different time points during a flagellar stroke. Assuming optimal subtraction time interval (Δt) and time window (T) are used (see discussion below, <FIG>), a higher frame rate leads to richer time-resolved information of speckle changes induced by motile parasites as well as more frames that can be used for averaging, thus can improve the SNR overall. However, because the goal is to generate contrast based on locomotion rather than high-fidelity recording of the beating patterns, frame rates that are below <NUM> fps are also acceptable for detection purposes. Considering the scanning time and the amount of acquired data, a frame rate of ~<NUM> fps may be successfully utilized for the imaging platform <NUM>. The performance of the imaging platform <NUM> may be improved using faster image sensors <NUM> and data interfaces to achieve higher frame rates, thereby improving the SNR without increasing the data acquisition time.

brucei is widely used as a model microorganism for the study of trypanosomes because it is non-pathogenic to humans and therefore safe to conduct experiments on. It is anticipated that the imaging platform <NUM> and methods disclosed herein will be readily applicable to Tb. gambiense, T. rhodesiense and T. cruzi, since their movements are fundamentally similar. Mouse blood and an artificial CSF solution were used throughout the testing due to safety concerns, but the lysis buffer also works with human blood. Future research may be conducted on testing patient samples from endemic regions to establish the sensitivity and specificity of the presented technique for the diagnosis of various trypanosomiases.

Numerous motile organisms can cause infections in humans. The imaging platform <NUM> and disclosed methods may also be configured to automatically differentiate different parasites. For instance, the amplitude and phase movie that is generated for each detected signal (see <FIG>) can allow a trained clinician to distinguish different motile parasites based on the morphology, size, and motility pattern, which is analogous to observing each live parasite under a brightfield and phase-contrast microscope. These may be optionally presented to the user on the GUI <NUM>. Prevalence of particular pathogens in the region can also aid in this regard. Moreover, a trained video classifier based on, e.g., a convolutional neural network (CNN) or a recursive neural network (RNN) can be utilized to distinguish and automatically identify various parasites, using sufficient training data. A schematic of an exemplary neural network <NUM> for determining characteristics of the motile parasites using the imaging platform <NUM> is illustrated in <FIG>. The trained neural network <NUM> analyzes the sequence of images <NUM> and determines characteristics of the motile parasites in the sample <NUM>, for example, the type(s) of parasites detected, 3D locations of the parasites in the 3D volume of sample <NUM> in the fluidic holder <NUM>, and a count of the parasite obj ects.

In the Examples, trypanosomes were utilized to demonstrate the feasibility of lensless time-resolved holographic speckle imaging to be employed in detection of parasitic infection. While the approach capitalized on the motility of trypanosomes, this platform is broadly applicable to other motile parasites, including other eukaryotic parasites such as T. vaginalis (see <FIG>, <FIG>), and other fluid samples beyond those tested here. In principle, this platform can also be used for the detection of Loa loa (L. loa) microfilariae in blood samples, which are significantly larger (~<NUM>-<NUM> in length) compared to the parasites that we studied here. For such large motile parasites, as an alternative approach, D'Ambrosio et al. used a cellphone-based detection method by taking advantage of the displacement of the RBCs caused by the collision with L. loa microfilariae in an imaging chamber. (See, <NPL>). This design is very compact and cost-effective, however it suffers from a much smaller detection volume (~<NUM>) compared to the method which screens and automatically processes ~<NUM> of whole blood or ~<NUM> of CSF, and it would be very challenging for it to be used for the detection of parasitic protozoa such as trypanosomes which have more than an order-of-magnitude smaller size and mass, lower parasitemia, as well as much weaker light scattering compared to L.

Motile bacteria also cause a number of human diseases. Although bacteria are typically much smaller than trypanosomes, the concept of motility-based detection combined with optical magnification may also be utilized for label-free detection of bacterial pathogens. There may be potential uses of motility-based detection for screening of other bodily fluids such as urine or diluted mucosal secretions and stool samples. Therefore, the imaging platform <NUM> and methods disclose herein have considerable potential to impact various global health challenges. Lastly, using motility as a biomarker and endogenous contrast can create new possibilities beyond clinical diagnostics. As a label-free 3D imaging modality that is robust to light-scattering and optically dense media, it can also be employed to study motile microorganisms within various fluid environments in a high-throughput manner.

Lysis buffer preparation: <NUM> sodium chloride (product no. <NUM>, Sigma Aldrich), <NUM> disodium phosphate (product no. <NUM>, Sigma Aldrich), <NUM> monopotassium phosphate (product no. <NUM>, Sigma Aldrich), <NUM> glucose (product no. G8270, Sigma Aldrich), and <NUM> % (w/v) sodium dodecyl sulfate (product no. L4390, Sigma Aldrich) in reagent grade water (product no. <NUM>-<NUM>-<NUM>, Fisher Scientific) were mixed for <NUM> hours using a magnetic stir bar on a magnetic mixer. The solution was then filtered using a disposable filtration unit (product no. <NUM>-<NUM>-65B, Fisher Scientific) for sterilization and was stored at room temperature. This buffer solution lyses all the components of whole blood including RBCs and WBCs but does not lyse the trypanosomes.

Artificial CSF preparation: According to a previous method, <NUM> sodium chloride, <NUM> sodium bicarbonate (product no. SX0320-<NUM>, EMD Millipore), <NUM> sodium phosphate monobasic (product no. S6566, Sigma Aldrich), and <NUM> potassium chloride (product no. P5405, Sigma Aldrich) were mixed well, and <NUM> magnesium chloride (product no. <NUM>, Sigma Aldrich) was added to make 10X artificial CSF. The solution was then filtered using a disposable filtration unit for sterilization. 10X stock solution was diluted ten-fold with reagent grade water to make 1X artificial CSF.

Culturing trypanosomes: <NUM>-derived bloodstream single marker trypanosomes (T. brucei) were cultivated at <NUM> with <NUM>% CO<NUM> in HMI-<NUM> medium with <NUM>% heat-inactivated fetal bovine serum (product no. <NUM>, Gibco) as described in <NPL>).

Collection of trypanosome infected mouse blood: All experiments involving mice were carried out in accordance with the guidelines and regulations of the UCLA Institutional Animal Care and Use Committee (IACUC), NIH Public Health Service Policy on Humane Care and Use of Animals, USDA Animal Welfare regulations, and AAALAC International accreditation standards under IACUC-approved protocol ARC# <NUM>-<NUM>. Mouse infections were performed as described in <NPL>), with the following modifications: Female BALB/cJ mice (product no. <NUM>, Jackson Laboratory, age <NUM>-<NUM> weeks) were injected intraperitoneally with <NUM>×<NUM><NUM>-<NUM>×<NUM><NUM> parasites in <NUM>-<NUM> ice-cold phosphate buffered saline with <NUM>% glucose (PBS-G). Parasitemia was monitored by counting in a hemacytometer, and infected blood samples were collected when parasitemia reached ~<NUM><NUM>-<NUM><NUM> parasites/mL. Infected blood was collected from either the saphenous vein or by cardiac puncture after euthanasia into heparinized capillary tubes (product no. <NUM>-<NUM>, Fisher Scientific) or heparinized collection tubes (product no. <NUM>, Covidien).

Separation of WBCs from human blood: Ficoll-Paque PREMIUM (product no. <NUM>-<NUM>-<NUM>, Fisher Scientific) was utilized for in vitro isolation of mononuclear cells from blood using density gradient separation according to manufacturer's instructions. Human blood samples were acquired from UCLA Blood and Platelet Center after de-identification of patients and related information and were used in the separation of WBCs from blood. <NUM> ethylenediaminetetraacetic acid (EDTA)-treated blood were mixed with <NUM> sterile PBS (product no. <NUM>-<NUM>-<NUM>, Fisher Scientific) in a <NUM> centrifuge tube (product no. <NUM>-<NUM>-<NUM>, Fisher Scientific) by drawing the mixture in and out of a pipette. <NUM> of Ficoll-Paque PREMIUM were placed in a <NUM> conical centrifuge tube (product no. <NUM>-<NUM>-53A, Fisher Scientific) and the diluted blood sample was carefully layered on the Ficoll-Paque PREMIUM. The suspension was centrifuged at <NUM>×g for <NUM> minutes at <NUM> using a centrifuge with swing-out rotors (Allegra X-22R, Beckman-Coulter). After centrifugation, the upper layer containing plasma and platelets was removed and mononuclear cells were transferred to a sterile centrifuge tube. To wash the cell isolate, it was mixed in <NUM> PBS and centrifuged at <NUM>×g at <NUM> for <NUM> minutes. The washing step was repeated twice, and the pellet was suspended in <NUM> PBS. The concentration of WBC was determined by counting in a hemacytometer and diluted accordingly to a stock solution of <NUM>×<NUM><NUM> WBC/mL in PBS.

Protocol for calibration curve analysis for blood samples: Freshly collected trypanosome-infected mouse blood was diluted in uninfected mouse blood (Balb/C, female, pooled, sodium heparin, Charles River Inc. ) to a concentration of approximately <NUM><NUM> parasites/mL. A sample of this trypanosome-infected blood was lysed with <NUM> volumes of lysis buffer and the trypanosome concentration was determined by counting in a hemacytometer. The trypanosome-infected blood was then diluted accordingly with uninfected blood to achieve the desired concentrations for calibration curve analysis.

Protocol for calibration curve analysis for CSF samples: Cultured trypanosomes were freshly harvested for each measurement to ensure consistent parasite motility. Trypanosomes were grown to a concentration of ~<NUM>×<NUM><NUM>-<NUM>×<NUM><NUM> cells/mL and harvested by centrifugation at <NUM>×g for <NUM> minutes. The cell pellet was resuspended in <NUM> of PBS-G and diluted approximately <NUM>-fold to <NUM><NUM> cells/mL in PBS-G. The trypanosome concentration was determined by counting in a hemacytometer and the sample was then diluted accordingly into 1X artificial CSF to achieve the desired concentrations for calibration curve analysis.

Sample preparation for imaging: The experiments were conducted using blood and artificial CSF samples. Borosilicate capillary tubes (inner dimensions: <NUM> height × <NUM> width × ~<NUM> length; product no. LRT-<NUM>-<NUM>-<NUM>, Friedrich & Dimmock, Inc. ) were prepared by dipping one end of the capillary tube (the fluidic holders <NUM>) into Vaseline jelly to plug the end. Plastic capillaries, e.g., those made of acrylic, can also be used instead of glass. Excess jelly was removed using a Kimwipe (product no. <NUM>-<NUM>, Fisher Scientific) and the tube end was sealed with parafilm (product no. <NUM>-<NUM>-<NUM>, Fisher Scientific). For each tube, <NUM> of sample was prepared. For blood samples, <NUM> of lysis buffer was mixed with <NUM> of uninfected or infected whole blood in a centrifuge tube. For CSF samples, <NUM>µL WBC stock solution was placed into trypanosome-infected artificial CSF to have <NUM>×<NUM><NUM> WBCs/mL (i.e., <NUM> WBCs/µL) in the final mixture. Each sample was mixed well by drawing the mixture in and out of a pipette before loading into the capillary tube. The open end of the capillary tube was then sealed using the jelly and parafilm. The glass capillary was then cleaned using a Kimwipe moistened with methanol (product no. A452SK-<NUM>, Fisher Scientific) and put on the device.

Culturing T. vaginalis: T. vaginalis strain G3 (Beckenham, UK <NUM>, ATCC-PRA-<NUM>) was cultured in modified TYM media supplemented with <NUM>% horse serum (Sigma), 10U/ml penicillin-<NUM>µg/ml streptomycin (Invitrogen), <NUM> ferrous ammonium sulfate, and <NUM> sulfosalicylic acid at <NUM> <NUM>. Culture was passaged daily and maintained at an approximate concentration of <NUM> × <NUM><NUM> cells/mL.

As shown in <FIG>, the imaging platform <NUM> is made up of five main modules: (<NUM>) a scanning head <NUM> having of three lensless holographic speckle imagers <NUM>, (<NUM>) a linear translation stage <NUM>, (<NUM>) scanning head housing <NUM>, (<NUM>) electronic circuitry <NUM>, and (<NUM>) a computing device <NUM> having a control program <NUM>, each of which is detailed below for the exemplary imaging platform <NUM> utilized in the Examples.

After the sample is loaded onto the imaging platform <NUM> and has settled for a <NUM>-<NUM> minutes waiting time (<NUM> minutes for lysed whole blood and <NUM> minutes for artificial CSF, see <FIG> for details), the user presses the "record" button on the GUI <NUM> to start acquisition. During screening, the device is programmed to scan the capillary tubes 120a, 120b, 120c at <NUM> discrete positions, with a distance of ~<NUM> between spatially adjacent ones. This results in a total screening volume of <NUM> (discrete scanning positions) × <NUM><NUM> (FOV of the image sensor) × <NUM> (channel height of the capillary tube) ≈ <NUM> per lensless speckle imager, and ~<NUM> for the three parallel imagers combined. At each of the <NUM> positions, to achieve a high frame rate (~<NUM> fps), the image sensor's FOV is split into two halves (i.e., the upper <NUM> rows and the lower <NUM> rows of the pixels), with each half capturing <NUM> frames (for lysed blood) or <NUM> frames (for CSF) sequentially (see <FIG>).

The temperature of the image sensor <NUM> rises when powered, leading to temperature gradient-induced convection flow of the liquid sample <NUM> (see <FIG>). To mitigate these problems, the scanning position stepping method and downtime between scanning positions method, as described herein, are utilized. As described above, instead of scanning the <NUM> positions unidirectionally, the imaging platform <NUM> scans in a back-and-forth fashion. Let the <NUM> positions be represented by positions #<NUM>, #<NUM>,. , #<NUM>, which are spatially ordered. Instead of scanning in the order of #<NUM>, #<NUM>,. , #<NUM>, the control program <NUM> programs the imaging platform <NUM> to scan with a larger step size of <NUM> positions, and whenever the scanning head <NUM> cannot move forward with this step size, it comes back to the unscanned position with the smallest position number. That is, the imaging platform <NUM> first scans positions #<NUM>, #<NUM>, #<NUM>, and #<NUM>. Then, the scanning head <NUM> comes back to position #<NUM>, followed by #<NUM>, #<NUM>, and #<NUM>, and so on. This scanning pattern largely prevents heat accumulation at a given section of the capillary tube <NUM>, which has sufficient time to cool down before the scanning head <NUM> comes back to its vicinity. As a second measure, a <NUM> second "downtime" is added between scanning positions to allow the image sensor <NUM> to cool down. After completing the acquisition at a given position, the power to the image sensor <NUM> is cut by a MOSFET-based digital switch 156a added into the USB <NUM> cable. After a <NUM> second wait time, the stepper motor <NUM> moves the scanning head <NUM> to the next position, where the power to the image sensor <NUM> is restored to capture the next set of images <NUM>.

During the testing of the Examples, the acquired images <NUM> are saved to an SSD <NUM> for processing. All three image sensors <NUM>, capturing uncompressed <NUM>-bit images <NUM>, generate a total data rate of ~<NUM> MB/s, which slightly exceeds the average write-speed of the solid-state drive (SSD). Therefore, a queue is created in the RAM <NUM> of the laptop computer <NUM> for each image sensor <NUM> to temporarily buffer the incoming image data, and another thread is created to constantly move the image data from the buffer into the SSD. However, because all the remaining image data can be fully saved to the SSD during the aforementioned downtime between positions, the total image acquisition time per test is not increased due to the limited write-speed. As a more time-efficient alternative, the acquired images <NUM> can be temporarily stored in the RAM <NUM>, while they are constantly moved to the GPUs <NUM> for processing in batches corresponding to each image sequence. In this way, the image processing can be performed concurrently with the image acquisition, reducing the total time per test (see Results above, <FIG>, and Table <NUM>).

The CMA algorithm <NUM> is used to generate 3D contrast from particle locomotion in noisy holograms and speckled interference patterns, and applies deep learning-based classification to identify the signals corresponding to the parasite of interest. As an example, <FIG> depicts the procedure used to detect trypanosomes from lysed whole blood, whereas in other application settings (e.g., trypanosome detection in CSF), minor changes are applied to the procedure, as detailed below. The CMA algorithm <NUM> takes the raw holographic diffraction patterns acquired by each image sensor at each scanning position as input. Ai (i = <NUM>,. , NF) are denoted as the raw frames, where NF is the total number of recorded frames in each sequence. In the experiments of the Example, the algorithm includes the following steps:.

For every <NUM>-bit raw image acquired by each image sensor (see <FIG>), it is divided by a "background" intensity pattern representing the non-uniformity of the laser diode illumination source, which was previously computed from Gaussian-smoothed and averaged raw images <NUM> in a negative control experiment and stored to be used by other experiments. After this, the hologram is further normalized (divided) by its own mean value, yielding the illumination-corrected holograms Ãi (i = <NUM>,. , NF) (see <FIG>).

In the case of lysed blood, because most of the cell debris tend to fully sediment within the <NUM> minute wait time (see <FIG>), "Tamura coefficient of the gradient" (ToG) autofocusing criterion is applied to automatically determine the z-distance of the bottom of the fluid sample (see <FIG>), denoted as zb. Then, the range of digital z-scanning as [zb - <NUM>, zb + <NUM>] is defined with a <NUM> step size. Note that in addition to the <NUM> expected channel height, an extra scanning range of ±<NUM> is given to allow for possible errors in the channel height, tilting of the channel, errors in autofocusing, etc. This makes the hardware much less complicated and inexpensive as it does not need tight tolerances in the scanning head <NUM> design and fluidic holder <NUM> placement.

In the case of clear media such as CSF where objects/particles are sparse, autofocusing to the bottom of the channel can be challenging. Therefore, the zb distance of each capillary tube is pre-calibrated (see below) and used throughout the experiments. Because the zb distance is pre-calibrated, i.e., not adaptively calculated for each sample, we specify a larger range of digital z-scanning, [zb - <NUM>, zb + <NUM>], also with a <NUM> step size. Note that zb is slightly different for each of the three channels of the device and is calibrated respectively.

The z-distances to be scanned are denoted as zj (j = <NUM>,. , Nz) as determined by the previous step. each element of Ãi is digitally propagated to each of zj with a high-pass filtered coherent transfer function (see <FIG>; see below for details) to obtain
<MAT>
where <IMG> represents the angular spectrum-based back-propagation, HP represents high-pass filtering, and i = <NUM>,. , NF, j = <NUM>,.

Next, time-averaged differential analysis with OFN is applied (see <FIG>), which yields:
<MAT>
where δF is the subtraction frame interval, exp <MAT> is the OFN factor, γ is a parameter related to OFN that is respectively tuned for lysed blood (γ = <NUM>) and CSF experiments (γ = <NUM>). Time-averaging significantly improves the SNR by smoothing out random image noise as well as random motion of unwanted particles/objects while preserving the true signals of motile microorganisms. OFN further suppresses potential false positive signals resulting from e.g., strongly scattering, unwanted particles/objects such as cell debris (see below and <FIG>, and <FIG>). The result of this step, Cj, is a three-dimensional image stack.

The z-stack Cj (j = <NUM>,. , Nz) suffers from a low-spatial-frequency background that mainly results from high-frequency noise in the raw images <NUM>, which remains when performing high-pass filtered back-propagation and frame subtraction. Therefore, as shown in <FIG>, the 3D z-stack Cj is first high-pass filtered in the z-direction by a mean-subtracted Gaussian kernel (σz = <NUM>) and the negative pixels are clipped to zero, yielding Dj (j = <NUM>,. It is then projected into a 2D image, E, using maximum intensity projection (MIP). High-pass filtering is applied again in 2D (mean-subtracted Gaussian kernel, σx = σy = <NUM>) to remove the residual background, and the negative pixels are again clipped to zero, yielding F.

Segmentation of candidate signal points within F is performed by 2D median filtering (<NUM>×<NUM> pixel window, pixel size = <NUM>), thresholding (threshold = <NUM> for detecting trypanosomes in lysed blood and <NUM> for detecting trypanosomes in CSF) followed by dilation (disk-shape structuring element, radius = <NUM> pixels, pixel size = <NUM>) and searching for connected pixel regions. Connected regions that are smaller than <NUM> pixels are discarded. <NUM>-by-<NUM> pixel image patches centered around the pixel-value-weighted centroids of these connected regions are cropped from F (without 2D median filtering), and are used for the downstream identification by a deep learning-based classifier.

A CNN that consists of three convolution blocks followed by two fully-connected layers is built and trained to identify true signal spots created by motile trypanosomes. The detailed network structure is shown in <FIG>, which is separately trained for trypanosome detection from lysed blood and CSF samples (see "Construction and training of the convolutional neural network (CNN) for the identification of trypanosomes" below for details).

The image processing steps (see <FIG>) are repeated for each captured image sequence and each image sensor <NUM>. The total detected number of trypanosomes from all the positions are summed up and divided by the total screened volume (~<NUM>) to calculate the detected parasitemia. For lysed blood, it is further multiplied by a factor of <NUM>, i.e., the dilution factor introduced by lysis, to calculate the parasitemia in the original whole blood sample.

The technique also offers the capability to locate the motile microorganisms in 3D and generate in-focus amplitude and phase movies of them for a close-up observation, using the following steps. For each signal spot that is classified as positive by the CNN classifier, using the corresponding z-stack Dj (j = <NUM>,. , Nz), only a "column" that is <NUM> × <NUM> pixels in x-y, centered around this spot, while spanning the entire z-range (Nz layers) is cropped out. Then, an autofocusing metric is used to evaluate each of the Nz layers, and the layer that corresponds to the maximum value of the autofocusing metric corresponds to its in-focus position. Both ToG and Tamura coefficient-based criteria were tried, and both work very well for this purpose. While the current z-localization accuracy is limited by the z-step size we chose (Δz = <NUM>), it can be further improved through finer z-sectioning. Using the currently found z-localization distance as an initial guess, high-pass filtered back-propagation and differential analysis (detailed in Step <NUM> CMA Algorithm, above) is performed over a z-range of ±<NUM> around the initial guess with a finer z-step size of <NUM>. However, OFN is disabled this time; in other words, the exponential normalization factor in Eq. <NUM> is removed, owing to OFN's side effect of slightly weakening the signal at the optimal focus distance, where the object function of the microorganism is the strongest. Autofocusing is performed again over the same <NUM> × <NUM>-pixel region over different z-layers similarly as before. The previously determined x-y centroid, in addition to the newly found z-distance, is used as the 3D location of this motile microorganism. Because the additional high-pass filtered back-propagation and differential analysis may be only performed on a smaller region-of-interest (ROI) around each given spot (e.g., in the experiments described herein, an ROI of <NUM>×<NUM> pixels is used), the 3D localization is computationally efficient. The 3D localization capability can be used to generate movies (detailed below), or to study microorganism behavior in biological or biomedical research settings.

Using the obtained 3D position of each motile microorganism, the movie of each detected microorganism can be generated by digitally back-propagating (without high-pass filtering) each frame of the recorded raw image sequence Ai (i = <NUM>,. , NF) or the illumination-corrected version Ãi to the corresponding z-coordinate. The amplitude and phase channels of the back-propagated images <NUM> are displayed side by side. The generated movies can potentially be used as an additional way to confirm the detection result of this platform when trained medical personnel are available.

Here, a laptop <NUM> equipped with an Intel Core i7-<NUM> central processing unit (CPU) <NUM> @ <NUM>, <NUM> GB of RAM was used, and two Nvidia GTX <NUM> GPUs <NUM> for image processing. Table <NUM> summarizes the time required for the image processing workflow, using a single GPU <NUM> or using two GPUs <NUM> simultaneously. Here, it is assumed that during image acquisition, the images <NUM> captured by the imaging device <NUM> are temporarily stored in the CPU RAM <NUM> and are constantly moved to the GPU memory in batches corresponding to the scanning positions, where it is processed by the GPU <NUM> (or GPUs). In this way, image processing can be performed concurrently during image acquisition, shortening the time requirement per test. This situation is mimicked by pre-loading existing data from the hard drive <NUM> into the RAM <NUM> of the computer <NUM> before starting the timer, which provides a reasonable estimation of the time cost of the processing. Because the number of acquired images <NUM> and the image processing workflow for lysed blood and CSF are different (see previous subsections and Methods), their timing results are calculated individually. In Table <NUM>, timing results for lysed blood and CSF are separately by "/".

To pre-calibrate the z-distance range for each of the three channels of the imaging platform <NUM>, one capillary tube whose bottom outer surface was purposely made dirty was installed. Then, three holograms was captured when the scanning head is at the two ends of its scanning range as well as in the middle, and autofocused to the dirty surface using the three holograms respectively<NUM>, <NUM>. The expected zb in this case was calculated from the averaged autofocusing distance by adding the wall thickness of the glass capillary tube. The calibration step needs to be done only once.

The diffraction patterns are back-propagated to the given z-distances using the angular spectrum method, involving a 2D fast Fourier transform (FFT), a matrix multiplication in the spatial frequency domain with the free-space transfer function, and an inverse FFT. However, because the approximate size of the trypanosomes is known, a high-pass filter is added into the transfer function in the spatial frequency domain to suppress other noises and artifacts.

The coherent transfer function of free-space propagation is given by
<MAT>
where z is the propagation distance, λ is the optical wavelength, fx and fy are spatial frequencies in x and y, respectively.

On top of H, two high-pass filters, H<NUM> and H<NUM>, are added to suppress unwanted interference patterns. H<NUM> is a 2D Gaussian high-pass filter, which is used to suppress the low-frequency interference patterns owing to the reflection from the various surfaces in the light path, including the protective glass of the image sensor and the various interfaces of the capillary tube loaded with fluids. H<NUM> is given by
<MAT>
where σ<NUM> = <NUM>. H<NUM> is used to suppress the interference patterns caused by the unwanted grooves of the manufactured glass capillary tubes. Because the grooves are oriented along the axial direction of the capillary tubes, corresponding to the y-direction in the captured images, their energy is mostly concentrated close to the fx axis in the spatial frequency domain. Therefore, H<NUM> performs high-pass filtering to fy, which is given by
<MAT>
where σ<NUM> = <NUM>.

The final coherent transfer function, which combines H, H<NUM> and H<NUM>, is given by
<MAT>
where min{H<NUM>, H<NUM>} chooses the smaller filter value from H<NUM> or H<NUM>.

The subtraction frame interval δF and total analyzed frames NF are parameters that should be optimized for the parasite (or microorganism) to be detected. δF and NF are related to the subtraction time interval Δt and the total analyzed time T through
<MAT>
<MAT>
where R is the frame rate of the recorded sequence (i.e., <NUM> fps in the system). By optimally choosing δF (or Δt), the signal from the characteristic locomotion of the microorganism of interest can be amplified with respect to the noise, which includes image sensor noise in addition to unwanted random motion of the background objects/particles in the sample. NF (or T), on the other hand, determines the window of time-averaging. A larger NF, in general, will result in reduction of the random background noise through averaging; but at the same time, it can potentially also weaken the useful signal if the microorganism swims away from its original location during T due to directional motion.

δF and NF are optimized for trypanosome detection by evaluating the average signal-to-noise ratio (SNR) of the processed images by CMA with OFN (corresponding to <FIG>), for blood and cerebrospinal fluid (CSF). Signal is defined as the maximum value of the segmented hotspot, whereas noise is defined as the average value of the background, excluding signal regions. SNR is calculated as <NUM>·log<NUM>(Signal/Noise) (dB). <NUM> hotspots were randomly chosen from one imaged field of view (FOV) of a <NUM><NUM>/mL trypanosome-spiked blood experiment, and <NUM> hotspots were randomly chosen from a <NUM><NUM>/mL trypanosome-spiked artificial CSF experiment. The SNRs were averaged for blood and CSF, respectively. δF and NF are varied to observe their effects on average SNR (see <FIG>). The same set of hotspots were used consistently for average SNR calculations as we varied δF and NF for either blood and CSF, respectively.

As shown in <FIG>, for lysed blood, δF = <NUM> (Δt = <NUM>) and NF = <NUM> frames (T = <NUM>) leads to the highest SNR of <NUM> dB. <FIG> implies that the average SNR could still increase if a larger NF (T) is used. However, due to practical time constraints of this platform as a diagnostic tool as well as heating of the sample over time, only <NUM> frames per image sequence are recorded.

For CSF (see <FIG>), a similar effect is observed regarding δF, which maximizes the SNR at δF = <NUM>. However, <FIG> shows that the SNR drops as a function of NF when NF > <NUM>, and therefore, the optimal NF is chosen as NF = <NUM>. In addition, the optimal SNR for CSF (δF = <NUM>, NF = <NUM>) is <NUM> dB, which is ~<NUM> dB higher than the optimal SNR in blood. Based on these observations, it can be concluded that because CSF is a clear medium (as compared to the lysed blood), less averaging (i.e., a smaller NF) is needed to achieve a low noise and high SNR. Therefore, for NF beyond <NUM>, the benefit in SNR from more averaging is diminished, whereas other factors that decrease SNR start to become dominant, mainly due to the gradual displacement of microorganisms from their original locations.

OFN is essential to reduce potential false positives owing to strongly scattering particles/objects within the sample (see <FIG> and <FIG> for a comparison of results with and without OFN). Under slightly time-varying illumination and drifting of the fluid, strongly scattering particles/objects such as cells that are not lysed, clumps of cell debris, spiked white blood cells (WBCs) in the CSF samples or even air bubbles can create strong contrast (hotspots) when processed by CMA. These hotspots can resemble those created by trypanosomes, leading to false positive detections. Therefore, in order to distinguish parasites of interest (especially trypanosomes) that have weak scattering and strong locomotion from other unwanted objects that have strong scattering and weak locomotion, we use the object function itself to normalize the frame subtraction corresponding to Eq. <NUM>. An exponential function with a properly selected γ further selectively suppresses strongly scattering objects. For trypanosome detection in lysed blood, γ = <NUM> is chosen based on visual judgement of the resulting distinction between "true positive" signals versus potential "false positive" signals; for trypanosome detection in CSF, γ = <NUM>.

Positive images are manually identified from experimental data with a relatively high concentration of spiked trypanosomes. For blood, one test (i.e., one scanning experiment with three capillary tubes) with a spiked trypanosome concentration of ~<NUM><NUM>/mL was used (no overlap with the data reported in <FIG>). For CSF, one test with a spiked trypanosome concentration of ~<NUM><NUM>/mL in artificial CSF was used, and the sample was not spiked with WBCs as was done for testing. For each bodily fluid type, the images were processed using the CMA algorithm <NUM> with OFN followed by post image filtering and segmentation (see description herein for details), and movies were generated for the first <NUM> detected candidate spots. Two human annotators jointly viewed these movies and judged the existence of a motile trypanosome in each movie characterized by a slender body and rapid beating. The resulting label for each movie was either "positive" (with a high confidence that there existed a moving trypanosome), "negative", or "uncertain". Multiple trypanosomes that coexist in a single video are labeled as "negative" to avoid confusing the network during training. It was much easier to annotate the movies related to CSF due to the high quality of the holographic reconstruction in the clear medium; whereas for blood, the resulting labels were mostly either "positive" or "uncertain", because it was difficult to affirm that the movie did not contain a trypanosome. After manual annotation, only those that were labeled as "positive" were kept in training/validation. The "uncertain" and "negative" were discarded. This resulted in <NUM> positive images for blood and <NUM> positive images for CSF. Note that the movies are solely for the purpose of labeling, and the 2D maximum intensity projection (MIP) image patches resulting from CMA are used to construct the training/validation library. The images were then randomly split into training and validation sets using a four-to-one ratio. Finally, data augmentation was performed to increase the number of training images by mirroring the images horizontally, vertically, and both horizontally and vertically, resulting in <NUM>× larger positive training libraries for blood and CSF, respectively.

Negative training/validation images entirely came from negative control experiments (no overlap with the data reported in <FIG>). One negative control test was used to populate the training/validation library for blood; two negative control tests were used for CSF because of fewer "false positives" per test. When segmenting the negative images, a lower intensity threshold was used (<NUM> for blood and <NUM> for CSF) to generate more images, resulting in <NUM> negative images for blood and <NUM> images for CSF experiments. The images were randomly split into training and validation sets using a four-to-one ratio for blood and CSF, respectively. Data augmentation was performed to the negative training libraries similarly to the positive set by mirroring the images in three different ways, resulting in a <NUM>× enlargement of the negative training library size. In addition, to improve the robustness of the trained classifier to unseen data, we also performed augmentation by replicating the negative images and multiplying by a factor of <NUM>. Thus, the total enlargement factor for the negative training libraries is <NUM>×.

A CNN <NUM> was constructed with three convolutional blocks and two fully connected (FC) layers (see <FIG>). Each convolutional block consists of a convolutional layer (filter size = <NUM>×<NUM>, stride = <NUM>, <NUM> channels) followed by a rectified linear unit (ReLU) layer and a max-pooling layer (filter size = <NUM>×<NUM>, stride = <NUM>). The first FC layer has <NUM> output nodes, and the second FC layer has <NUM> output nodes, representing the two classes (trypanosome and non-trypanosome). The outputs are then passed through a softmax layer to generate the class probabilities. Dropout (p = <NUM>) is added to the first FC layer during training. The same network architecture is separately trained to identify trypanosome signals within blood and CSF.

The CNN <NUM> was implemented in TensorFlow (version <NUM>. <NUM>) and Python (version <NUM>. The convolutional kernels were initialized using a truncated normal distribution (mean = <NUM>, standard deviation = <NUM>×<NUM>-<NUM>). The weights of the FC layers were initialized using the Xavier initializer. All network biases were initialized as zero. The learnable parameters were optimized using the adaptive moment estimation (Adam) optimizer with a learning rate of <NUM>-<NUM>. A batch size of <NUM> was used, and the network was trained for ten thousand iterations until converged.

The CMA algorithm <NUM> was accelerated using CUDA C++ and was run on a laptop computer <NUM> with dual Nvidia GTX <NUM> graphics processing units <NUM> (GPUs). The most computationally intensive mathematical operations in the CMA algorithm <NUM> were fast Fourier transforms (FFTs) and inverse FFTs (IFFTs). The Nvidia CUDA Fast Fourier Transform library (cuFFT) library was used to perform these operations. Thrust library was used to perform reduction (i.e., summation of all elements) of an image, which was further used to calculate the mean value of the image for normalization. Other various basic mathematical operations on real or complex-valued images were implemented using custom-written CUDA kernel functions. The CUDA code was carefully optimized to parallelize computation, maximize efficiency and minimize GPU memory usage. For instance, when performing back-propagation of the diffraction patterns to each z-distance, the high-pass-filtered coherent transfer function (Equations <NUM>-<NUM>) was only calculated once per z-distance, which was reused for all the frames in the time sequence. When performing time-averaged differential analysis with OFN (Eq. <NUM>), only (δF + <NUM>) back-propagated images (i.e., Bi) needed to be stored in the GPU memory at each given time without sacrificing performance, which reduced the GPU memory usage and made it possible to process even larger-scale problems (e.g., image sequences with more frames, or performing CMA at more z-distances) or to use lower-end GPUs with less memory.

Before performing FFTs, the raw images <NUM> (vertical: <NUM> pixels, horizontal: <NUM> pixels) were padded to a size of <NUM> × <NUM> pixels. The padded pixels were assigned the mean value of the unpadded image to reduce artifacts from discontinuities. Because the new dimensions are powers of <NUM> and <NUM> (<NUM> = <NUM><NUM> × <NUM> and <NUM> = <NUM><NUM>), the FFT operation was accelerated by a factor of ~<NUM>× compared to without padding. After IFFT was complete, the images <NUM> were cropped back to the original size for other image processing steps.

The temperature of the image sensor <NUM> rises when it is turned on, creating a temperature gradient above it. Therefore, the fluid sample <NUM> within the glass tube <NUM> will gradually start to flow, also causing the particles within the glass tube to move directionally. As a result, the signal of motile microorganisms generated by the CMA algorithm <NUM> will weaken due to a "smearing" effect; and in the meantime, the movement of the other unwanted particles will increase the background noise and false positive detections, which is undesirable. The fluid sample <NUM> velocity due to convection is related to the height of the fluid channel. Due to the drag force near the channel wall, a thinner channel will lead to a reduced fluid velocity at a given time after the onset of heating. However, as a tradeoff, a thinner channel also results in a reduced screening throughput.

COMSOL Multiphysics simulation software was used to estimate the flow speed inside the channel. As shown in <FIG>, a channel (<NUM> inner height, <NUM> inner width, surrounded by a silica wall with a uniform thickness of <NUM>) filled with water was created. A <NUM> section of the channel was selected as the region of interest for simulation. At the center of the channel, a CMOS image sensor <NUM> (modeled as a constant heat source with a surface temperature of <NUM>) was placed <NUM> below the channel's bottom surface. ~<NUM> was the highest temperature of the image sensor <NUM> during image acquisition (see Methods), experimentally measured by an infrared camera (FLIR C2). The reference temperature (room temperature) was set to be <NUM>. Non-isothermal flow was used to model the water inside the channel and the air outside the channel.

<FIG> show the result of this simulation. The maximum fluid velocity magnitude inside the channel is shown, representing a worst-case scenario. As expected, the fluid velocity increases as a function of the time after onset of heating and the channel height (see <FIG>). The relation between the maximum fluid velocity and the channel height in (c), at t = <NUM> after the onset of heating was further plotted, which approximately corresponds to the duration of image acquisition (there is a time gap when switching from the upper half to the lower half of the image sensor's FOV). Again, the fluid velocity at t = <NUM> represents a worst-case scenario, where the fluid velocity is largest. As shown in <FIG>, at a channel height of <NUM>, the fluid velocity is ~<NUM>/s. Over the duration of a single image sequence of <NUM> frames (~<NUM>), the displacement due to fluid flow is upper-bounded by ~<NUM>, which is acceptable when compared with the length of the trypanosome. On the contrary, if the channel height is <NUM>, the displacement will be upper-bounded by ~<NUM>, which will lead to strong smearing and reduction of the signal. As a result, the channel height used in the experiments conducted herein was chosen as <NUM>.

Claim 1:
An imaging platform (<NUM>) for the detection of motile objects of interest (<NUM>) in a fluid sample (<NUM>) comprising:
one or more substantially optically transparent sample holders (<NUM>);
a moveable scanning head (<NUM>) containing one or more coherent light sources (<NUM>) located on a first side of the one or more substantially optically transparent sample holders (<NUM>) and one or more corresponding image sensors (<NUM>) associated with the one or more coherent light sources (<NUM>) and located on an opposing side of the one or more substantially optically transparent sample holders (<NUM>), wherein the one or more coherent light sources (<NUM>) project light onto the one or more substantially optically transparent sample holders (<NUM>);
a translation stage (<NUM>) configured to translate the moveable scanning head (<NUM>) relative to the one or more optically transparent sample holders (<NUM>);
a computing device (<NUM>) configured to receive a movie of time-varying holographic speckle pattern image sequences obtained by the one or more image sensors (<NUM>), the computing device (<NUM>) comprising computational motion analysis software configured to generate a three-dimensional (3D) contrast map of candidate motile objects within the one or more optically transparent sample holders (<NUM>), the computing device (<NUM>) further comprising a deep learning-based classifier software to identify the motile objects of interest (<NUM>) in the three-dimensional (3D) contrast map, wherein the deep learning-based classifier software is trained to distinguish motile objects of interest (<NUM>) from a group of candidate motile objects.