A portable brightfield and fluorescence microscope toward automated malarial parasitemia quantification in thin blood smears.

Malaria is often most endemic in remote regions where diagnostic microscopy services are unavailable. In such regions, the use of rapid diagnostic tests fails to quantify parasitemia measurements which reflect the concentration of Plasmodium parasites in the bloodstream. Thus, novel diagnostic and monitoring technologies capable of providing such information could improve the quality of treatment, monitoring, and eradication efforts. A low-cost, portable microscope for gathering quantitative parasitemia data from fluorescently stained thin blood smears is presented. The system employs bimodal imaging using components optimized for cost savings, system robustness, and optical performance. The microscope is novel for its use of monochromatic visible illumination paired with a long working distance singlet aspheric objective lens that can image both traditionally mounted and cartridge-based blood smears. Eight dilutions of red blood cells containing laboratory cultured wild-type P. falciparum were used to create thin smears which were stained with SYBR Green-1 fluorescent dye. Two subsequent images are captured for each field-of-view, with brightfield images providing cell counts and fluorescence images providing parasite localization data. Results indicate the successful resolution of sub-micron sized parasites, and parasitemia measurements from the prototype microscope display linear correlation with measurements from a benchtop microscope with a limit of detection of 0.18 parasites per 100 red blood cells.

Introduction

The aim of this study is to test a novel portable fluorescent microscope that may have utility for quantitative parasitemia measurement to inform malaria diagnosis. Despite improvements in global malaria infection and mortality rates over the past several decades, eradication progress has slowed since 2014, and the disease remains a global healthcare crisis with an estimated 228 million cases and 405,000 deaths annually [1]. Patients located in remote regions, which can be the most endemic for the disease, are often limited in their ability to receive the high-quality, laboratory microscopy diagnosis used in well-established hospitals and clinics [2, 3]. In the absence of better options, the presumptive, symptomatic-based diagnosis of malaria can have undesirably high inaccuracy and contribute to wasted resources, poor care, and the proliferation of antimalarial drug resistances [4, 5]. In the past decades, rapid diagnostic tests (RDT’s) have been developed to screen for Plasmodium parasites in the blood with sensitivity at or below 200/μL, but they are unable to quantify parasitemia, or the concentration of parasites in the blood, which is used by clinicians to assess infection severity and monitor patient response to treatment. Recent guidance by the Malaria Eradication Research Agenda highlights the need for improved technological tools and data to guide diagnostic and eradication strategies in the coming decades [6].

Obtaining accurate parasitemia measurements is especially important for infections with P. falciparum, the deadliest and most common species of Plasmodium to infect humans, due to their ability to rapidly progress to complicated states [7]. Death from severe malaria often occurs within hours of admission for treatment, so it is essential that therapeutic doses of a highly effective antimalarial drug be administered quickly. Patients who are determined to be at a higher risk may be monitored more closely and placed onto more aggressive forms of treatment, whereas more conservative treatments may be prescribed for patients deemed to be low-risk [8]. While parasitemia alone is not an indication for disease severity, it can provide useful information to clinicians as they diagnose patients. High parasitemia in the absence of incapacitation can be an indicator that the disease may progress rapidly to a complicated state, especially in the presence of risk factors such as pregnancy, being below the age of 6–10, human immunodeficiency virus infection, malnutrition, or anemia [8]. This research aims to improve the quality of malaria diagnosis, treatment, and monitoring in remote regions by proposing and testing a low-cost, portable multimodal microscopy system.

Conventional parasitemia measurements collected according to the World Health Organization Malaria Microscopy Guide seek to provide a “reasonably and acceptably accurate” parasite count by comparing the ratio of parasites to leukocytes in thick blood films [9]. The accuracy of the method is hence highly variable and is limited by the assumptions that patients have 8,000 leukocytes and 5,000,000 red blood cells (RBC’s) per microliter of blood. Many labs will take the additional step of verifying such parasitemia values against red blood cell counts obtained via hemocytometry. Values of parasitemia can also be defined by comparing the number of parasites to either red blood cells or directly to blood volume, which are also limited by their dependence on the precision of the assumptions of RBC’s per microliter and the exact volume of blood sampled, respectively. To preserve clarity, this work will measure parasitemia by directly comparing the number of parasites to the number of red blood cells in a sample; either as the percent of infected RBC’s or as an equivalent ratio of parasites per 100 RBC’s, which is more descriptive of counts that include extracellular parasites in sample analysis.

In determining the desired lower bound of detection, it is impractical to use the true lower range of parasitemia found in patients, since it is sub-detectable using even conventional benchtop microscopy. Typical lower limits of microscopic detection from the literature range anywhere from 5–100 parasites per microliter, which is accomplished by examining thick blood smears. Approaching this same limit of detection is unfeasible using thin smear examination due to the high number of RBC’s necessary to achieve statistical significance. It is most practical in this case to consider what lower bound of detection is implied by the diagnostic necessity when the microscope will be used as intended as a supplement to RDT screening. With the primary aim of disease diagnosis being to prescribe the proper course of treatment for each patient, the system need only be able to quantify parasitemia insomuch as it assists clinicians in differentiating between uncomplicated cases, complicated cases, and those in danger of rapidly progressing into a complicated state. The microscope is most applicably useful, therefore, in identifying patients with high parasitemia but who do not present with obvious complicated symptoms. For this reason, the target measurement range for the system was decided to be between 0.01% - 1% of cells infected.

Taking microscopy out of a central laboratory for global health application is not a recent advancement in the field. In the 1930’s-1950’s, McArthur described the benefits of point-of-care brightfield microscopy [10-12]. A popular, recent trend leverages cell phone technology–their network access, processing power, onboard cameras, and other built-in sensors–to construct low cost, simple microscopes [13]. However, the ever-changing camera optics and small aperture lenses make their versatility, control, and quality assurance difficult to achieve [14]. In the time since MacArthur, various portable microscopes have been created and commercialized to varying degrees of success [15, 16]. In particular, the potential for fluorescence imaging to augment the signal-to-noise ratio (SNR) of portable microscopy has born several innovative diagnostic imaging systems, with each offering their own contributions to the progression of the field. Beginning in the 1990’s, several systems presented various benefits and limitations to using fluorescence for malaria imaging [17-19]. During the next decade, the development of the CyScope® marked the first significant commercialization of a fluorescence microscope for malaria diagnostics in the field, although it relied heavily on manual operation and used conventional compound microscope optics [20-22]. In the past decade, the overall size of portable fluorescent systems has begun to shrink, utilizing new optical configurations, and their performance have begun to be evaluated in field settings [23-25]. More recently, the advent of machine-learning based image processing platforms has been extended to the interpretation of results from fluorescence scans of Plasmodium infected blood films [26].

Microscope design and construction

Microscope configuration

The microscope described here represents the completed third-generation system developed by the author’s laboratory group, with several intermediary stages presented in conference proceedings [27-29]. Significant changes from the second-generation system include the use of lower cost optical components chosen by tuning magnification and resolution requirements, use of a higher-quality camera and more robust computational platform, and the elimination of cross-polarized imaging which was determined to add unnecessary cost and complexity. The microscope was designed to be bimodal, with brightfield imaging used to assess cellular size and boundaries and fluorescence imaging, which has been shown to be effective and less variable than Giemsa staining, used to detect intracellular parasites [23, 30]. The system was designed to resolve the features necessary to detect Plasmodium inside red blood cells from both conventional thin smears created on glass microscope slides and previously published thin smears generated using pumpless microfluidic cartridges [31]. To promote design simplicity, the center wavelength of the brightfield illumination source light emitting diode (LED) was set at 520 nm, in alignment with the fluorophore emission peaks of dyes used in the study. The use of a monochromatic system minimized the effects of chromatic aberrations and allowed for the use of singlet lenses where typically doublet or triplet lenses would be required for broadband systems. In order to resolve micron-sized features at λ = 520 nm, an objective numerical aperture of 0.40 was selected to provide theoretical resolution of 793 nm according to the Rayleigh criterion. Zemax OpticStudio ray tracing models were used to design the illumination optics, infinity-corrected imaging system, choose off-the-shelf components, and verify performance prior to prototype construction. An overview of the microscope configuration can be seen in Fig 1.

Fig 1

(A) Layout of optical components in the portable microscope. CAD models of microscope: (B) optomechanical mounts and electronic control components, (C) extruded aluminum frame, and (D) fully encased prototype demonstrating clamshell design. (E) Final physical prototype microscope.

Prototype construction

The objective lens chosen based on modeling and experimental validation was a Geltech 0.40 numerical aperture (NA) glass aspheric singlet with 6.24 mm focal length (LightPath Technologies, Orlando, FL; #355110). A 1.6 MP monochromatic Blackfly S camera with a Sony IMX273 sensor (FLIR Systems Inc, Wilsonville, OR) was chosen for its combination of price, sensor and pixel sizes, frame rate, SNR, and well-defined programmatic interface. A bi-convex spherical singlet with 100 mm focal length (Newport Corp., Irvine, CA; #KBX064) was chosen as a tube lens, resulting in images being 16.03x magnified with a field-of-view (FOV) measuring 250 μm x 333 μm in the object plane, which allowed the full width of the previously cited microfluidic channels (250 μm) to be surveyed in a single image capture. A 523-nm monochromatic LED was chosen for transmission illumination (Osram, Germany; # LZ1-00G102-0000), and fluorescence excitation was provided by a 485 nm LED (Cree, U.S; #XPEBBL-L1-0000-00301). The fluorescence filter set (Semrock, U.S; FITC-LP01-Clinical-000) was chosen for compatibility with SYBR Green-1 dye that was used to label parasites in smears and contained a dichroic mirror with 500 nm cut on/off, 475/28 nm excitation bandpass filter, and emission long-pass filter with cutoff at 515 nm. Glass aspheric lenses with NA 0.79 were used to collect and condense the light from the transmission LED source onto the object plane (Thorlabs, Newton, NJ; #ACL25416U), and a singlet biconvex lens (Newport Corp, Irvine, CA; #KBX043) was used to collect epi-illumination light for fluorescence excitation. To reduce the footprint of the system, a two-inch diameter planar aluminum mirror (Thorlabs, Newton, NJ; #ME2-G01) was used to fold the brightfield illumination pathway and a one inch planar aluminum mirror (Thorlabs, Newton, NJ; #PFSQ10-03-G01) was used to fold the imaging pathway.

The prototype microscope was assembled using 3-D printed optomechanical components and was housed inside a case constructed from one-inch square aluminum rails and acrylonitrile butadiene styrene (ABS) plastic shrouds, with exterior dimensions measuring approximately 30 cm x 20 cm x 13 cm (Fig 1B–1E). The final device weighs approximately 6.5 lbs. The primary optical axis of the microscope was oriented vertically to allow samples to be held level during imaging. The brightfield illumination system was housed in the lid of the microscope, and the imaging and control components were housed in the base. To operate, the lid was opened, a sample placed onto the stage, then the lid was closed before imaging. Interchangeable clips were used to hold either conventional 75 mm x 25mm glass slides or the aforementioned microfluidic cartridges, giving the microscope adaptability to either sample preparation format (Fig 2). With the lid closed, a compressive force is applied to the interchangeable clips, holding samples to the translation stage and ensuring alignment with the focal plane of the microscope. Sample translation and focal control utilize one stepper motor each (Sparkfun, Boulder, CO; # ROB-09238) to change fields-of-view and fine tune focus as needed. A Jetson Nano ARM-based single-board computer (Nvidia, Santa Clara, CA) was chosen as the onboard data collection and processing system due to its low cost and ability to facilitate future implementations of automated image processing based on machine learning algorithms. The system is currently powered using a dedicated power supply but can be adapted to operate from rechargeable battery packs for field deployment. A bill of materials for all major optical and electronic components is presented in S1 Table along with associated costs. The current prototype microscope system costs approximately $1,300, excluding the cost of non-critical components likely to be significantly changed during manufacturing such as fastening hardware, case rails, 3-D printed mounts, and wiring cables/adapters.

Fig 2

(A) A glass slide-mounted thin smear positioned in the open microscope, ready for imaging. The top motor assembly translates the sample while bottom motor controls objective focus. (B) Side-by-side comparison of clips for either glass or microfluidic smears. (C) Blood cells being pipetted into a microfluidic channel in the microscope.

Statistical analysis

A statistical analysis was conducted to determine the appropriate target parasitemia dilutions and inform the statistical significance of results depending on the sample size and density of parasites. A linear regression using Pearson’s product moment correlation method was used to compare the values of parasitemia measured by the portable and benchtop fluorescent systems. If the determination of whether a specific red blood cell is infected is represented as a binomial event, the probability of whether red blood cells in a sample are infected with parasites can be assumed to follow a Bernoulli distribution. Given this assumption, it is possible to consider the relative percent error of a given parasitemia measurement at the edge of its 95% confidence interval for any given sample size. This relationship, being a function of two independent variables, can be represented as a surface over parasitemia and sample size, with the percent error of the confidence interval increasing with both lower parasitemia and lower sample size. A contour plot of this surface generated in MATLAB software using the Clopper-Pearson method is shown in S1 Fig, representing sample size as the equivalent number of microscopic FOV assuming the average cellular smear density (670 RBC per FOV) that was found using the bimodal microscopy system.

Experimental procedure

P. falciparum wild-type strain 3D7 was obtained through BEI Resources (Manassas, VA; # MRA-102) and maintained in continuous in-vitro culture by a modification of the Trager and Jensen method [32]. AB+ human erythrocytes (Gulf Coast Regional Blood Center, Houston, TX) were used to suspend the parasites in a 4% hematocrit solution in complete medium containing 5 g Albumax II (Gibco Life Technologies, Auckland, NZ), 2 g sodium bicarbonate (EM Sciences, Gibbstown, NJ), 20 mg hypoxanthine (Sigma-Aldrich, St. Louis, MO), 16.2 g RPMI 1640 (contains 25 mM HEPES) (Gibco, Grand Island, NY), and 0.25 mL penstrep (Gibco, Grand Island, NY) per liter. Individual 5 mL cultures were grown in each well of a 6-well plate (Corning, Kennebunk, ME; # 3471) and incubated in a chamber flushed with a gas mixture of 2% O2, 5% CO2, 93% N2 at 37°C.

Wild-type P. falciparum parasites were cultured in cell media for nine days until erythrocytic life-cycle stages were desynchronized and multiple morphologies could be found simultaneously in test smears. From this culture, parasitized red blood cells were centrifuged in two-minute increments at 0.2 x 1,000 rcf to form a loose pellet of concentrated RBC’s. These pellets were then extracted, collected into a single tube, and the process repeated twice again until cell media was separated from the parasitized RBC’s. This concentrated culture pellet was resuspended in equal volume fresh human blood plasma to create a parasitized human blood analog. From images of Giemsa-stained control slides collected using 60x oil-immersion benchtop microscopy, the parasitemia of the raw culture was measured to be 1.2 ± 0.1% using digital counting of manually identified parasites and RBC’s in ImageJ/FIJI software [33]. Various parasitemia dilutions were created based on the assumption of culture parasitemia equal to 1.2 parasites/100 RBC’s (1.2%) by mixing volumes of this reconstituted parasitized culture with whole human blood. Dilutions were created to target parasitemia levels of 1% (50,000/μL), 0.75% (37,500/μL), 0.5% (25,000/μL), 0.25% (12,500/μL), 0.1% (5,000/μL), 0.05% (2,500/μL), 0.01% (500/μL), and un-parasitized blood (0.0% parasitemia). To promote the consistency of thin-smear quality, all data gathered for parasitemia quantification in this study used conventionally made smears on standard glass microscope slides. Fluorescent staining solution for dry thin smears was created by dissolving SYBR Green 1 stock at 1:8,000 dilution in 1x Tris-HCL buffer pH 8. To stain, previously fixed thin smears were flooded with 500 μL of SYBR stock solution and allowed to incubate for five minutes in the dark. Afterwards, smears were rinsed for 10 seconds with de-ionized water and allowed to air dry in the dark.

The portable microscope was connected to an auxiliary display monitor, keyboard, and mouse to control the user interface used to capture images, activate LED’s, change exposure settings, and translate the sample. Alternatively, an iPad, laptop, or other device could be used to remotely operate the microscope through secure shell client software. Smears from each dilution were imaged first on the portable system, then on the benchtop system, with fluorescent images captured prior to brightfield imaging for each FOV to minimize the effects of photobleaching during brightfield image capture. Brightfield images were captured immediately afterwards, then the sample was translated to the next FOV and the process repeated until the usable monolayer region was exhausted. Manual inspection was used to identify whether or not a given FOV contained an even monolayer of cells, all FOV found to contain a multilayer were bypassed by advancing to the following adjacent area on the slide and were thus excluded from data collection. This process could be automated in future control software development to increase the usability of the microscope by minimally trained personnel. In thin blood smears, cells tend to cluster more thickly on the proximal portions of the slide and thinly toward the distal regions, with a prime monolayer region tending to exist in between. To account for these heterogeneities in smear thickness, samples were initially mounted on the portable microscope stage with the FOV on one side of the monolayer region, and the slide was advanced linearly through the prime imaging area. Focusing of the objective was necessary before the first image was captured for each smear but re-focusing was typically unnecessary throughout the imaging of each individual slide. Due to the need to confirm proper focus of each FOV prior to imaging, all sample translation and focal adjustments are manually controlled using preset functions entered in the Python command terminal of the onboard Jetson Nano. Control of the responsible stepper motors could be easily automated with the development of auto-focusing functionality. After each smear was imaged on the portable system, the process was immediately repeated on a benchtop Nikon Ti-82 inverted fluorescence microscope using a similar commercial objective lens (20x, 0.45 NA, Nikon #MRH48230) and FITC fluorescence filter set, with fluorescence images again captured prior to brightfield images. The turnaround time for digitization of each FOV was not measured as a variable during experimentation. All data were saved from the portable microscope directly to a USB flash drive, and the uncompressed images were then transferred to a laptop computer for retrospective analysis.

Ethical statement

This work represents early-stage technological and methodological development for potential medical diagnostic purposes. It has not yet been vetted in clinical studies and should not be used as guidance for medical diagnoses or treatments. The authors declare to have no financial interests or conflicts of interest in the technologies or methods herein described.

Results

Optical characterization

Brightfield images of a 1951 US Air Force Resolution Target (USAFT) (Fig 3A) and sections of sub-resolution fluorescent features (Fig 3C, inset) were used to derive the experimental resolution capabilities of the microscope. The system is fully capable of resolving sub-micron features, as shown by the visible distinction of all group nine USAFT elements (line widths = 0.98, 0.87, 0.78 μm), implying that the system approaches the theoretical diffraction limit of 0.793 μm for 0.40 NA imaging systems at λ = 520 nm (Fig 3B). Further analysis of the US Air Force Target images shows that the net system magnification is experimentally measured to be 16.03x, matching the theoretical magnification predicted by the ratio of the tube lens focal distance (f = 100 mm) to that of the objective lens (f = 6.24 mm). At this magnification, each camera pixel represents 0.215 μm x 0.215 μm of object space, allowing the camera to satisfy the digital sampling requirements according to the Nyquist Sampling Theorem for features above 430 nm in size. The microscope’s point-spread-function (PSF) was measured in sections through eight separate sub-resolution point features in fluorescence images (Fig 3C). After all normalized PSF’s were averaged together, the full-width-half-max distance was calculated using linear interpolation to be 4.47 pixels, or 0.963 μm.

Fig 3

(A) Image of 1951 US Air Force Target captured using the portable microscope. (B) Profiles from rectangular sections taken across elements 9–1: 0.98 μm; 9–2: 0.87 μm; and 9–3: 0.78 μm are shown at right. (C) Point spread function of the microscope measured using averaged profiles through sub-resolution fluorescent features (inset).

Quantitative parasitemia measurement

Thin blood smears used during investigation were measured to contain an average of 670 red blood cells per FOV on the portable microscope. When comparing brightfield images of the same sample gathered on both the prototype microscope and the benchtop control, the benchtop system showed more contrast at cell boundaries (Fig 4). Fluorescent images from the prototype system showed higher background fluorescence than their benchtop counterparts. Image exposure was tuned to be 100 ms for fluorescence images and 15 ms for brightfield images to prevent image saturation by maximally bright features for each modality. Subsequent brightfield and fluorescent images were collected on both systems with minimal pixel shifting between modes, allowing simple cross-comparison and image addition to create composite images that can be used to co-locate parasites and RBC’s in a single frame.

Fig 4

Brightfield (top) and fluorescence (bottom) images from the same FOV of Smear #3 for the benchtop control (left), and portable (right) microscopes. Images have been cropped and linearly contrast adjusted for clarity. Source benchtop images contain 4,275 cells and 44 parasites (1.0%) and source portable images contain 833 cells and 16 parasites (1.6%). The cropped images shown in the figure contain 593 cells.

Red blood cell and fluorescent objects in all images were counted using ImageJ/FIJI software, and data were processed using MATLAB software. The same object and RBC counting procedures were used for the generation of both the reference parasitemia measurements collected using the benchtop microscope and the experimental measurements from the portable system. All object data from images have been provided as a MATLAB workspace in the Texas Data Repository (https://doi.org/10.18738/T8/UBJTY1) along with the MATLAB script used to classify objects. RBC’s per image were counted using the local maxima of the Laplacian transform of background-subtracted images, and parasites were counted in fluorescence images using a two-step segmentation procedure. Initial fluorescent object definition utilized automated intensity thresholding from background-subtracted images for segmentation, then final parasitic objects were separated from the population of all fluorescent features using thresholds set using the distributions of object maximum and mean pixel intensities, size, and circularity. The thresholds were set by the same investigator for all samples by using images from the raw culture smear as a baseline. All thresholds were verified against parameter distributions from manually segmented parasites used as classification controls. These thresholds were then automatically applied to all dilutions for image analysis. Object maximum pixel intensity was found to be the most discriminating feature for the differentiation of parasites and non-parasites as most fluorescent artifacts displayed peak fluorescence intensities below those of parasites.

After tuning the segmentation thresholds, the number of parasites and red blood cells from each field of view were tabulated, as shown in Table 1. From these data, the ratio of parasites to total red blood cells was plotted for the portable system and the benchtop system to assess the agreement in parasitemia measurements between the two systems (Fig 5). A linear regression was fit to the dataset to quantify the degree of agreement between the two sets of measurements, with adjusted R2 = 0.939 and slope = 0.996. Parasitemia results are displayed as parasites per 100 RBC’s to reflect the inclusion of all fluorescent objects determined to meet the criteria for being parasites, regardless of whether they were located inside or outside of blood cells. A comparison of images for high, medium, and low parasitemia from the portable microscopes can be found in S2 Fig.

Table 1

Sample sizes of all smears, cells, and fluorescent objects.

		Portable Microscope				Benchtop Microscope
Smear	Parasitemia Dilution	# FOV	# Cells	Raw Objects	Filtered Objects	# FOV	# Cells	Raw Objects	Filtered Objects
1	1.200%	21	12477	455	207	6	15441	754	244
2	1.000%	26	13998	203	203	3	8247	107	107
3	1.000%	19	12903	197	182	7	25817	406	294
4	0.750%	50	30923	290	287	8	39139	215	215
5	0.500%	10	7231	53	53	4	18703	98	98
6	0.500%	25	17524	90	90	5	24216	111	111
7	0.250%	9	6635	23	23	2	7045	43	16
8	0.250%	24	14401	69	52	6	22843	103	88
9	0.100%	20	15355	64	64	5	23089	43	43
10	0.100%	17	11528	44	35	5	26413	45	42
11	0.050%	25	22557	51	51	5	28322	32	15
12	0.050%	13	9657	37	24	4	18215	33	33
13	0.010%	35	18593	115	115	10	42366	85	85
14	0.010%	32	24145	104	92	5	22662	64	64
15	0.000%	18	13456	17	17	6	29521	4	4
16	0.000%	10	6763	15	15	2	5248	9	2

Fig 5

Correlation between parasitemia measurements collected using the portable and benchtop microscopes.

The fit line indicates a linear regression with reported R2 value adjusted for the number of independent variables, and dashed lines bound the regression 95% confidence interval.

Discussion

Advantages & novelty

Results of this early-stage study indicate that the prototype microscope can quantify parasitemia with high correlation to that provided by benchtop microscopy. The dynamic range of the system encompasses a range of parasitemia outcomes in which most high-risk infections might exist, enabling the likely identification of patients requiring urgent treatment when used in conjunction with rapid diagnostic screening tests. The system successfully uses several novel features for portable malaria microscopes–notably the use of a singlet aspheric objective lens capable of sub-micron resolution with a working distance of over 6mm. Use of this objective lens is possible due to the monochromatic illumination wavelength used which minimizes the presence of chromatic aberrations which would otherwise significantly distort images. Such a long working distance expands the flexibility of the system to image through additional types of samples and substrates, including conventionally mounted thin and thick blood smears and the cartridge-based smears previously mentioned. Typically, sample preparation is a major limitation for fluorescence microscopy because of the potential for artifact contamination, but the negative impact of staining contamination could be minimized by this microscope’s compatibility with new, more self-contained sample preparation mechanisms.

To the author’s knowledge, this work presents one of the lowest cost portable microscopy platforms capable of collecting and automatically processing quantitative parasitemia data for malaria patients at the point-of-care, although the eventual costs of commercially ready products are difficult to assess based only on preliminary prototypes. As a platform technology, the cost of the microscope should be divided across the usable lifetime of the device, allowing the net cost per test to be primarily driven by the cost of renewables (slides, cartridges, stains, etc.). Base model benchtop fluorescence microscopes retail for >$3,000 USD and these systems are not portable. Further, while simple compound benchtop microscopes can retail for $300–1,000 USD, these systems are also not generally portable nor capable of fluorescence imaging and do not have the onboard computational power required to provide the automation necessary to allow parasitemia quantification to be possible in remote settings by those without extensive skill and infrastructure. The prototype microscope presented here is comparable in price to previously published portable medical tools such as the Lab-In-A-Backpack system (~$1,500) [34].

Limitations

Several important limitations to the described microscopy system must be noted as well. This study should be considered to be early stage, as it demonstrates the theoretical feasibility of a novel optical microscopy system for use in malaria diagnostics. The study was conducted using a prototype system that is not commercially viable and did not examine samples from actual patients, which may display greater heterogeneity. Additionally, these experiments were not blinded and were conducted in a controlled laboratory without the presence of confounding environmental factors likely encountered in an actual use case. The harshness of field testing may necessitate design revisions such as kinematic features to ensure optical alignment of the illumination system when the lid is closed. One environmental challenge that will need to be solved during field studies is the storage and transport requirements of the fluorescent stain. An alternative stain, Acridine Orange, may be substituted for SYBR Green-1 if a viable storage solution cannot be reached. Another limitation of this study is the lack of qPCR (quantitative polymerase chain reaction) use as a control standard for all microscopic measurements. When examining the results of the study, comparison of the intended parasitemia dilutions, which were generated using counts from traditionally Giemsa-stained samples, with the experimentally measured fluorescence values shows apparently higher numbers of parasites throughout in fluorescently stained samples. This discrepancy likely results both from A) the increased sensitivity of fluorescence staining to detect early stage and extracellular parasites that are not counted in traditional brightfield imaging and B) the inclusion of false-positive artifacts in the smear. Despite this, the benchtop and portable microscope proved to be comparable in their ability to detect fluorescent features in thin smears, demonstrating the performance of the portable optical system.

While displaying linear agreement with a benchtop equivalent, the data from using the portable microscope show a baseline false-positive rate of approximately 0.2 parasites per 100 RBC’s, or 10,000 parasites per microliter. This lower limit of quantification should be sufficient to inform diagnostic decisions between complicated and uncomplicated cases, but it is limited to relatively high parasitemia cases and must be further improved for standalone use for confirmatory diagnoses. In its current state, the microscope must still be used primarily for parasitemia quantification as an adjunct to RDT-based parasite detection. It is expected that this lower limit of detection could be further improved using more sophisticated fluorescent feature classification that utilize the computational capabilities of the onboard Jetson Nano platform. Additionally, due to the fact that the number of FOV containing a monolayer of cells in a smear is dependent on the quality of the smear, there are inherent limitations to the possible number of cells to be surveyed, and thus the possible upper limit of accuracy, even if the entire smear is scanned. This dependency underscores the potential advantages of any sample preparation techniques that increase the quality of thin smears such that imaging and analysis can be conducted consistently.

Conclusions

Results of this study show the imaging performance of a prototype microscope designed for the quantitative measurement of parasitemia in thin smears of Plasmodium infected blood. Quantitative parasitemia measurements have potential utility for monitoring disease severity both before and after the administration of treatment. The system demonstrates marked improvement in providing quantifiable data and compatibility with a variety of sample types compared to previous iterations of the system as earlier referenced. Further studies are needed, primarily in field-based settings, so that the diversity of possible hematological and contextual conditions can be encapsulated. Given its moderate-to-high limit of detection and the widespread adoption of RDT’s in malaria diagnostic practices, it is anticipated that, once fully developed, this microscopy technique would be most effectively deployed as an adjunct to RDT’s. In practice, the portable microscopy system could be utilized after P. falciparum infections are first confirmed using RDT’s, creating a dual-technology method for efficiently screening and thoroughly diagnosing patients at the point of care.

Relative measurement error.

Contour plot of relative measurement error at the edge of the measurement 95% confidence interval for varying parasitemia and sample sizes. Parasitemia is defined as the number of parasites per 100 red blood cells, and the sample size is listed in number of fields of view examined assuming the average of 670 cells per field of view.

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High, medium, and low parasitemia images.

Composite images from portable microscope showing smears with: (A) high 1% parasitemia, 833 cells, 13 parasites; (B) medium 0.1% parasitemia, 1005 cells, 4 parasites; (C) low 0.01% parasitemia, 833 cells, 2 parasites. The brightfield image from each was inverted and linearly contrast enhanced for clarity, and the fluorescent image overlay shows the segmented object pixels overlaid using the green image channel using FIJI ImageJ software.

(TIF)

Click here for additional data file.

Costs of major components in portable microscope prototype.

(PDF)

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14 May 2021

PONE-D-21-12786

A portable brightfield and fluorescence microscope toward automated malarial parasitemia quantification in thin blood smears

PLOS ONE

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1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: N/A

**********

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: PLOS ONE Review

Manuscript PONE-D-21-12786

Summary

The interesting manuscript entitled ”A portable brightfield and fluorescence microscope toward automated malarial parasitemia quantification in thin blood smears" by Paul David Gordon et al describes a digital system for P. falciparum malaria diagnostics where a low-cost, portable digital slide scanner is used to digitize fluorescently-stained blood smears, and the digital images analysed to detect and quantify P. falciparum parasites and red blood cells (RBCs). The system is evaluated by the analysis of thin blood smears, prepared from laboratory-prepared erythrocyte blood cultures with various levels of P. falciparum infection. Both the brightfield and fluorescent channels from each digitized field of view are analysed to detect parasites and RBCs, using a proprietary digital image algorithm. This digital analysis is performed based on segmentation of objects of interest (parasites in the fluorescent image and RBCs in the brightfield image) based on thresholds determined by manual analysis of the images. Overall, the results presented demonstrate a strong correlation in the quantification of parasites in the samples digitized by the proposed instrument, as compared to analysis of the same samples, digitized with a high-end reference digital microscope (Nikon Ti-82). As malaria remains a significant global health problem, novel technologies for improved field diagnostics are needed, and the technology presented here might therefore contribute to improving malaria diagnostics. The technology is aimed especially for point-of-care diagnostic utilization in low-resource areas where the burden of disease is typically the highest.

Overall, the technology presented by the authors is exciting, and the results encouraging. The quality of writing is very good, and the structure of the manuscript is generally quite good. Technical information regarding the hardware are presented in high detail, although some information regarding e.g. the development of the software is lacking. Although the findings are encouraging, and the research goal is laudable, I do however have several questions regarding the study methodology. Furthermore, although the results are presented as preliminary, I do believe some of the mains conclusions are not supported by the findings.

Comments:

1. Abstract - Please provide more information regarding study methodology in the Abstract. What types of samples were analysed and how were they prepared (i.e.thin smears, fluorescently stained, laboratory culture samples), how many samples were used, specific results (level of correlation) and reference standard used (visual or digital analysis of samples?). Also, avoid speculation in the Abstract which are not related to the research findings (e.g. here compatibility with machine learning solutions, this is not explored in the study).

2. Application and Potential Impact – I would consider moving this section (or the relevant information from this section to the Discussion)

3. Ethical statement - Although the submission information states that no ethical information is required, I would considering adding this information (as well as why no information is required) as a short statement to the manuscript body, as this is an article exploring a medical diagnostic technology.

4. I noticed your interesting previous work in this field from recent years (e.g. (https://spie.org/Publications/Proceedings/Paper/10.1117/12.2510593?print=2 and https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6997630/)

• Is the same system used? Presumably these findings relate in some way, I would consider including a reference or brief summary on your previous work in this field. Have you updated the hardware, made technical adjustments or addressed other challenges?

5. Introduction (and Discussion) - Interestingly, we have published a study last year describing a very similar, also quite low-cost system with quite comparable imaging performance (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242355). As you no doubt are aware, there are a number of other similar systems, as several other papers have investigated systems for field digitization of e.g. fluorescently stained samples (e.g. already in 2009 by Breslauer et al.), followed by e.g. CyScope and the Parasight system. I would consider briefly reviewing what the findings of previous work in the field have been to provide the reader with perspective and better understand the context and implications of your work. This is especially important as you state that this is the lowest cost similar system (the price seems to be approximately comparable to certain other solutions, although understandably it is difficult to establish a final price based on a prototype)

6. Page 6, line 116 - Please define abbreviation SNR (signal-to-noise ratio).

7. Line 151, page 8 – It seems Figure 2 and 3 have accidentally been interchanged (also e.g. Line 220, page 11)

8. Lines 187 – 201 - I would appreciate to read some more details about the operation of the system. How is the sample scanning performed – is the sample translated automatically or by manual adjustment of the sample to change FOV before digitization of the next one?

• How was the field of view (FOV) with the monolayer region selected?

• How large are the areas which are digitized (is the complete slide digitized)?

• Approximately how many RBCs and parasites were on average analyzed per slide (this is perhaps included in the MATLAB library online, but I would consider this information quite important to interpret the results, and therefore include some information also in the manuscript). If only a small image region is analyzed, the results might differ quite a lot based on the area which is analysed.

• Line 194 – what is an “adequate number of images” and how was this amount decided? Did it differ between samples?

• What was the turnaround time for digitization and analysis of one slide?

9. Methods and Results - How many thin smears did you analyze, i.e. what was the sample size and how was it decided? Was a single slide analyzed for each parasitemia dilution level? How were the statistical calculations performed, what was the level of significance and what software was used?

10. Methods - Although I understand that the parasitemia dilution levels were available from the preparation of the samples, these are apparently not used as a reference (e.g. Fig 5). It is a bit unclear to me what the reference standard is, e.g. is the Benchtop Parasitemia Measurement determined by manual (visual) analysis of the digital sample with the high-end microscope, or is the same digital image analysis software used, similarly to the low-cost solutions? If manual assessment of samples was used, how was the parasitemia level determined?

11. Methods - What samples were used to develop the software used for analysis of the samples, i.e. what samples did you use to determine the threshold settings to separate e.g. artefacts from parasites? Were these samples images from the same samples you analyzed, or completely different samples? Preferably the training data should be separated from the validation data.

• During our similar work, we found one unfortunately quite significant challenge to be the separation of artefacts from parasites by analysis of only the fluorescent samples, especially in conventional thin smears collected in the field (see e.g. S3 Fig https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242355#sec018, which represents a field sample). By brightfield analysis of Giemsa stained sample using high-power (60-100x) microscopy, it is possible to more reliably confirm the parasites, but e.g. the spatial resolution in Figure 4 seems to suggest that this would be quite challenging to do using the levels of magnification here. How did you confirm e.g. that the fluorescent signals with lower intensities that were classified as artefacts were not trophozoites? Although this is briefly discussed on page 12, perhaps e.g. image examples here could illustrate the difference between artefact and parasite more clearly.

12. Methods - How were digitized images stored – locally or uploaded to the internet? What is the file format and level of compression, and are the images saved as individual FOVs or stitched to whole-slide images? A figure demonstrating a “scanned sample” would perhaps illustrate this. Can the system operate in regions without network access?

13. Methods - As the system is aimed for rural, low-resource regions, can the system operate on battery power or does it require a stable power source? Naturally, this could likely of course be solved e.g. by using an external battery

14. Methods - Is the staining technique described here possible to perform in rural settings with access only to basic laboratory equipment, or would it need to be altered to be usable in low-resource environments?

15. Line 264, page 13 - “Preliminary results” - I would consider rephrasing to e.g. “early results” or clarify why these are preliminary. How do these results relate to your previous work (see earlier comments)?

16. Line 267, page 13 – As you have implicated, a detection level of 0.2 is quite low for clinical utilization and does not e.g. allow the ruling out infections. Please clarify how this is “sufficient to inform most diagnostic decisions” (i.e. in rural setting clinical malaria diagnostics, perhaps one of the most important clinical questions is the differential diagnostics of malaria vs other conditions).

17. Line 288, page 14 - Considering moving Table 1 to the Methods section (description of the system) or e.g. Supplementary Information and avoid presenting new information in the Discussion.

18. Line 290, p. 15 - “Results of this study show the feasibility of a method for quantitative parasitemia measurement while away from a centralized microscopy laboratory using an automated, low-cost bimodal microscope optimized for the specific task”. I do not agree that the results suggest this. The reason is that the sample are neither collected, nor prepared (to my understanding) while away from a centralized laboratory. Although the samples are thin smears, I would consider it quite unlikely to be directly comparable to thin blood smears collected in field settings. To my understanding, the results seem to suggest instead that the imaging performance of the presented system seems to be sufficient for determination of the parasitemia level in moderate- to high-level samples, which are prepared in laboratory settings. I therefore think it is premature to draw conclusions regarding usage especially at more remote points of care. I would consider rephrasing this to e.g. instead state that the system is designed for usage at the point of care.

19. Discussion – Please further discuss the strengths and weaknesses of the study, as well as the challenges required before clinical implementation. E.g. although it is stated that the technology could be combined with RDTs for diagnostic applications, it should be emphasized more clearly that the detection level is quite high and needs to be improved to allow confirmatory diagnostics.

20. Figure 1 – Please include size of the system in the image (width, length, height) and weight. This could be included in the Methods section also.

21. Figures - Although the data from all findings are made available, is any image data available (e.g. illustrating the detected parasites in e.g. a low-, compared to a high-level parasitemia sample? This would make the analysis of the samples more understandable also for non-technical readers, such as clinicians.

Reviewer #2: Summary of the research and overall impression

This well-written manuscript describes the results of a laboratory-based study to compare a newly developed portable malaria microscopy device with bench-top microscopy on blood samples spiked with cultured plasmodium falciparum parasites. The study shows that the new portable device is able to detect and quantify parasitemia to a similar degree as the bench-top microscope. The authors conclude that this technique could be used in combination with RDT’s to monitor treatment efficacy in malaria endemic settings and that it could contribute to screening for antimicrobial resistance.

The results section shows figures of the device and the images the device can produce compared to the images produced by a bench-top microscope. It is not entirely clear to me if the device provides a clear-cut interpretation of results, or if a technician is still needed to interpret the data, though the title of the manuscript suggests the former. It furthermore provides a linear regression model in which the quantification results from the bench-top microscope are compared to the portable microscope.

The manuscript furthermore provides a detailed insight into how the device was developed. The added value of the portable device over regular bench-top microscopy would be the fact that it is automated (I assume) and that it is (as the name suggests) portable, and therefore can be moved without having to be calibrated before use. If this technique is finetuned (a baseline false-positive of 10.000 p/uL is rather high) and works in a rural setting it could prove useful in malaria management.

The main issue of this study to me is the fact that the authors draw their conclusions based on a relatively small non-blinded laboratory-based experiment without testing the device in field settings and clinical samples.

- How do the microfluidic cartridges work (these would be a major advantage compared to the use of thin slides as to create a good thin slide requires some skill)

- A blinded study using spiked samples

- A blinded study using clinical samples

- Preferably a time-series before and after treatment

- Preferably a field study performed in field settings to see how it copes with differences in temperature, humidity, dust etc.

Second, I think the manuscript should focus more on the experiment itself and the results of the experiment. At the moment the only explicit data shown as to how it functions compared to other techniques is a figure to compare images made by bench-top microscope and the device, and one linear regression analysis.

This is why I suggested a major revision. Personally, I would like to see additional experiments done before these conclusions are drawn. If the authors would adjust the conclusions to better reflect the actual data presented here a minor revision would be justified. I think that the device has potential, but I also think that it's potential is very limitedly shown in the current data.

General Comments

- A baseline false-positive of 10.000 p/uL is a very high baseline, limiting the usefulness of this technique as it is presented at the moment, and should be acknowledged as such. Particularly taken into consideration that RDT’s have to be able to detect parasitemia at 200 p/uL and that field microscopy is generally able to detect parasitemia counts lower than that

- The experiment was not blinded which should be explicitly stated.

- Samples were not tested in duplicate

- Parasitemia levels in the dilutions were assumed rather than tested

Other comments

- The manuscript places emphasis on technical details on how the portable microscope was designed. As a result, it can be challenging to read for a broader audience.

- It would be useful if the authors would consistently provide both percentage infected RBC and p/uL or choose one of the two and leave out the other. Right now both are used interchangeably which makes it more difficult to compare

- To me it seems like there are a lot of methods in the results section and some new data (e.g. the cost overview as well as the statement that the LOQ was 10.000 p/uL) in the discussion which I found somewhat confusing, though it is a minor remark.

In addition to the comments mentioned above

Abstract

- In my opinion the authors should be careful with their hypothesis that the technology may be useful in drug resistance screening. First of all, the technique itself does not screen for resistance but rather assesses efficacy of therapy. Second, the current study does not provide data to support this claim as they did not test follow-up, nor any clinical samples. In this, the technique is no different from any other which can quantify parasitemia.

Introduction

- Line 57; low parasitemia counts do not necessarily correspond with mild disease and vice versa; I would nuance this statement.

- Line 66: I would nuance this by adding that in many labs data are corrected for bloodcell counts as obtained by hemocytometry.

- It would be useful to add the aim of the current study to the introduction (the current study describes …..) and not just the ultimate goal of the technique that is being developed (line 60 onward). This makes it easier for the public to screen what they are about to read which was a lot more technical than I had anticipated from the title and abstract.

Methods

- Please describe how many samples were tested and what their parasitemia level was according to the culture. Please state how these relate to the portable microscope results

- Please describe in more detail how test results were interpreted (automated or not) and who interpreted the results (same person for both tests?)

- The FOV without monolayer cells excluded: did this significantly decrease the number of fields included in the analysis? Please provide numbers on how many fields were captured per slide and how many were excluded. Please state in the discussion how this would affect the results

- I wonder if logistic regression comparing test results of two tests which were (as I assume) interpreted by thesame researcher is an appropriate choice. If possible, it would be nice to compare with qPCR instead.

Results

- It would be nice to see an image of how the results are presented to the technician.

Discussion

- I am missing a limitations section

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Reviewer #1: Yes: Oscar Holmström, MD, PhD

Reviewer #2: No

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26 Oct 2021

The authors wish to sincerely thank the Reviewers for their excellent and thorough feedback, especially as it relates to clarifying the purpose of the work and incorporating it into the broader field. The authors also thank the editors and reviewers for their patience as revisions have been completed.

Overall, there have been many minor revisions that should improve the robustness of the work and the clarity of communication for the work that has been done. The primary changes were to emphasize that the work was meant to be a technical description of a new technology rather than a description of a diagnostic test itself. Nearly all comments were accepted and implemented in the paper.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

6 Dec 2021

26 Feb 2022

Thank you again for your excellent feedback. The comments from both reviewers were entirely appropriate and appreciated.

With specific regards to the comments on the potential for live parasite imaging, although it is not a part of this work, the separate publications on micro-fluidic channel smears hold some promise in this regard, although the technology needs further improvements to be deployable. We welcome further feedback and discussion on this topic in the future as appropriate.

Submitted filename: Response to Reviewers-02.pdf

Click here for additional data file.

22 Mar 2022

A portable brightfield and fluorescence microscope toward automated malarial parasitemia quantification in thin blood smears

PONE-D-21-12786R2

Dear Dr. Gordon,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Academic Editor

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #3: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: I Don't Know

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #3: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #3: Yes

Reviewer #4: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #3: I thank the authors' effort in improving this manuscript, which is ready to be published. I found the manuscript easy to read and concise. I just have one more suggestion to the authors, in line 165, can the authors change 6.5lbs to kg, just to be consistent with other units?

Reviewer #4: All comments have been addressed well. The writing is fluent and results are clearly presented. I would recommend for acceptance.

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Reviewer #3: No

Reviewer #4: No

30 Mar 2022

PONE-D-21-12786R2

A portable brightfield and fluorescence microscope toward automated malarial parasitemia quantification in thin blood smears

Dear Dr. Gordon:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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