Community mobilisation is a vital process for raising awareness and increasing participation in healthcare interventions, research, and programmes that require human sample collection and mass management. In this report, we present the community mobilisation approach undertaken for the implementation of the operational mapping and assessment of granular schistosomiasis and soil-transmitted helminths in Ekiti State, Nigeria. The mobilisation was conducted in 177 communities/wards of the 16 local government areas. A total of 15,340 urine and stool samples were collected in 34 days. The efficacy and success of the strategy were evaluated through the following three performance metrics: community compliance rate, the participant response rate at the community level, and the overall compliance response rate of the four most sensitive LGAs. Community compliance was 93.7% as sample collection was denied in nine communities and two other communities demanded the return of the collected samples despite our mobilisation effort because of cultural bias and myths that connect the collection of stool and urine samples to ritual activities in the local context. The participant response rate at the community level was 86.7%. Three of the four sensitive LGAs (based on previous assessment programmes) demonstrated satisfactory compliance rates of 100%, while a response rate of 64.0% was computed for one of the LGAs. We believe our approach contributed to effective community mobilisation and awareness and that the developed model has the potential to improve participation rates in large healthcare assessments and intervention programmes.@en

Traditionally, automated slide scanning involves capturing a rectangular grid of field-of-view (FoV) images which can be stitched together to create whole slide images, while the autofocusing algorithm captures a focal stack of images
to determine the best in-focus image. However, these methods can be timeconsuming due to the need for X-, Y- and Z-axis movements of the digital microscope while capturing multiple FoV images. In this paper, we propose a solution to minimise these redundancies by presenting an optimal procedure for automated slide scanning of circular membrane filters on a glass slide. We achieve this by following an optimal path in the sample plane, ensuring that only FoVs overlapping the filter membrane are captured. To capture the best infocus FoV image, we utilise a hill-climbing approach that tracks the peak of the mean of Gaussian gradient of the captured FoVs images along the Z-axis. We implemented this procedure to optimise the efficiency of the Schistoscope, an automated digital microscope developed to diagnose urogenital schistosomiasis by imaging Schistosoma haematobium eggs on 13 or 25 mm membrane filters. Our improved method reduces the automated slide scanning time by 63.18%and 72.52% for the respective filter sizes. This advancement greatly supportsthe practicality of the Schistoscope in large-scale schistosomiasis monitoringand evaluation programs in endemic regions. This will save time, resources andalso accelerate generation of data that is critical in achieving the targets for schistosomiasis elimination.@en

In this research, we report on the performance of automated optical digital detection and quantification of Schistosoma haematobium provided by AiDx NTDx multi-diagnostic Assist microscope. Our study was community-based, and a convenient sampling method was used in 17 communities in Abuja Nigeria, based on the disease prevalence information extracted from the baseline database on schistosomiasis, NTD Division, of the Federal Ministry of Health. At baseline, samples from 869 participants were evaluated of which 358 (34.1%) tested S. haematobium positive by the reference diagnostic standard. Registered images from the fully automated (autofocusing, scanning, image registration and processing, AI image analysis and automatic parasite count) AiDx assist microscope were analyzed. The Semi automated (autofocusing, scanning, image registration & processing and manual parasite count) and the fully automated AiDx Assist showed comparable sensitivities and specificities of [90.3%, 98%] and [89%, 99%] respectively. Overall, estimated egg counts of the semi-automated & fully automated AiDx Assist correlated significantly with the egg counts of conventional microscopy (r = 0.93, p ≤ 0.001 and r = 0.89, p ≤ 0.001 respectively). The AiDx Assist device performance is consistent with requirement of the World Health Organization diagnostic target product profile for monitoring, evaluation, and surveillance of Schistosomiasis elimination Programs.

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In this research, we report on the performance of automated optical digital detection and quantification of Schistosoma haematobium provided by AiDx NTDx multi-diagnostic Assist microscope. Our study was community-based, and a convenient sampling method was used in 17 communities in Abuja Nigeria, based on the disease prevalence information extracted from the baseline database on schistosomiasis, NTD Division, of the Federal Ministry of Health. At baseline, samples from 869 participants were evaluated of which 358 (34.1%) tested S. haematobium positive by the reference diagnostic standard. Registered images from the fully automated (autofocusing, scanning, image registration and processing, AI image analysis and automatic parasite count) AiDx assist microscope were analyzed. The Semi automated (autofocusing, scanning, image registration & processing and manual parasite count) and the fully automated AiDx Assist showed comparable sensitivities and specificities of [90.3%, 98%] and [89%, 99%] respectively. Overall, estimated egg counts of the semi-automated & fully automated AiDx Assist correlated significantly with the egg counts of conventional microscopy (r = 0.93, p ≤ 0.001 and r = 0.89, p ≤ 0.001 respectively). The AiDx Assist device performance is consistent with requirement of the World Health Organization diagnostic target product profile for monitoring, evaluation, and surveillance of Schistosomiasis elimination Programs.@en

Purpose: Automated diagnosis of urogenital schistosomiasis using digital microscopy images of urine slides is an essential step toward the elimination of schistosomiasis as a disease of public health concern in Sub-Saharan African countries. We create a robust image dataset of urine samples obtained from field settings and develop a two-stage diagnosis framework for urogenital schistosomiasis.

Approach: Urine samples obtained from field settings were captured using the Schistoscope device, and S. haematobium eggs present in the images were manually annotated by experts to create the SH dataset. Next, we develop a two-stage diagnosis framework, which consists of semantic segmentation of S. haematobium eggs using the DeepLabv3-MobileNetV3 deep convolutional neural network and a refined segmentation step using ellipse fitting approach to approximate the eggs with an automatically determined number of ellipses. We defined two linear inequality constraints as a function of the overlap coefficient and area of a fitted ellipses. False positive diagnosis resulting from over-segmentation was further minimized using these constraints. We evaluated the performance of our framework on 7605 images from 65 independent urine samples collected from field settings in Nigeria, by deploying our algorithm on an Edge AI system consisting of Raspberry Pi + Coral USB accelerator.

Result: The SH dataset contains 12,051 images from 103 independent urine samples and the developed urogenital schistosomiasis diagnosis framework achieved clinical sensitivity, specificity, and precision of 93.8%, 93.9%, and 93.8%, respectively, using results from an experienced microscopist as reference.

Conclusion: Our detection framework is a promising tool for the diagnosis of urogenital schistosomiasis as our results meet the World Health Organization target product profile requirements for monitoring and evaluation of schistosomiasis control programs.
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For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings.

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We have applied a combination of blind deconvolution and deep learning to the processing of Shack-Hartmann images.By using the intensity information contained in spot positions, and the fine structure of the separate images created by the lenslets,we have increased the sensitivity and resolution of the sensor over the limit defined by standard processing of spot displacements only.We also have demonstrated the applicability of the method to wavefront sensing using extended objects as a reference.

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We investigate the general adjustment of projection-based phase retrieval algorithms for use with saturated data. In the phase retrieval problem, model fidelity of experimental data containing a non-zero background level, fixed pattern noise, or overexposure, often presents a serious obstacle for standard algorithms. Recently, it was shown that overexposure can help to increase the signal-to-noise ratio in AI applications. We present our first results in exploring this direction in the phase retrieval problem, using as an example the Gerchberg-Saxton algorithm with simulated data. The proposed method can find application in microscopy, characterisation of precise optical instruments, and machine vision applications of Industry4.0.

@en

For many parasitic diseases, the microscopic examination of clinical samples such as urine and stool still serves as the diagnostic reference standard, primarily because microscopes are accessible and cost-effective. However, conventional microscopy is laborious, requires highly skilled personnel, and is highly subjective. Requirements for skilled operators, coupled with the cost and maintenance needs of the microscopes, which is hardly done in endemic countries, presents grossly limited access to the diagnosis of parasitic diseases in resource-limited settings. The urgent requirement for the management of tropical diseases such as schistosomiasis, which is now focused on elimination, has underscored the critical need for the creation of access to easy-to-use diagnosis for case detection, community mapping, and surveillance. In this paper, we present a low-cost automated digital microscope—the Schistoscope—which is capable of automatic focusing and scanning regions of interest in prepared microscope slides, and automatic detection of Schistosoma haematobium eggs in captured images. The device was developed using widely accessible distributed manufacturing methods and off-the-shelf components to enable local manufacturability and ease of maintenance. For proof of principle, we created a Schistosoma haematobium egg dataset of over 5000 images captured from spiked and clinical urine samples from field settings and demonstrated the automatic detection of Schistosoma haematobium eggs using a trained deep neural network model. The experiments and results presented in this paper collectively illustrate the robustness, stability, and optical performance of the device, making it suitable for use in the monitoring and evaluation of schistosomiasis control programs in endemic settings. @en

To achieve the WHO’s goal of sustained elimination of Lymphatic Filariasis disease, there is the need for accurate diagnostic device especially in areas of low-intensity infections. This project, which is a collaboration of the Nigerian Federal Ministry of Health (FMoH), AiDx and funded by THE END FUND is exploring the performance capacity of the novel AiDx_NTDx Assist automated microscope in the detection of microfilaria – Wuchereria bancrofti in prepared blood samples (glass slides) using standard microscopy as a reference. The prevalence of circulating Wuchereria bancrofti microfilaria in blood was assessed using automated Image based microscopy analysis for the detection of microfilariae in prepared blood smears.

1250 people in four LGAs of Ogun state, Nigeria participated in this study. All prepared blood samples analyzed by both expert manual microscopy and the AiDx NTDx Assist results showed that none of the 1250 participants samples analyzed had any presence of W. bancrofti microfilariae in their blood. Since no positive samples was detected by the reference test and the AiDx NTDx Assist, it was impossible to estimate the sensitivity of the device. However, based on the negative results obtained, the AiDx NTDx Assist showed a specificity of 100%, an accuracy of 100% and a Negative Predictive Value of 100%.

Despite the baseline report obtained from the National data base of the ministry of health, indicating the prevalence of 10%, 8.2%, 4.2% and 4% in the four local government areas where samples were collected, we were not able to find a participant with detectable microfilaria. Evaluation of the AiDx NTDx Assist device shows direct correlation with the expert manual microscopy. Although samples were taken in remote/rural areas of some of the LGA, e.g., Adodo Ota, result obtained however suggest a deviation from baseline and reality. This may be due to previous MDA undertaken in 2018 as reported by the state NTD officers. Further, thorough reassessment is therefore recommended .@en

Significance: Particle field holography is a versatile technique to determine the size and distribution of moving or stationary particles in air or in a liquid without significant disturbance of the sample volume. Although this technique is applied in biological sample analysis, it is limited to small sample volumes, thus increasing the number of measurements per sample. In this work, we characterize the maximum achievable volume limit based on the specification of a given sensor to realize the development of a potentially low-cost, single-shot, large-volume holographic microscope.

Aim: We present mathematical formulas that will aid in the design and development and improve the focusing speed for the numerical reconstruction of registered holograms in particle field holographic microscopes. Our proposed methodology has potential application in the detection of Schistosoma haematobium eggs in human urine samples.

Approach: Using the Fraunhofer holography theory for opaque objects, we derived an exact formula for the maximum diffraction-limited volume for an in-line holographic setup. The proof-of-concept device built based on the derived formulas was experimentally validated with urine spiked with cultured Schistosoma haematobium eggs.

Results: Results obtained show that for urine spiked with Schistosoma haematobium eggs, the volume thickness is limited to several millimeters due to scattering properties of the sample. The distances of the target particles could be estimated directly from the hologram fringes.

Conclusion: The methodology proposed will aid in the development of large-volume holographic microscopes.@en

Schistosomiasis is a neglected tropical disease of Public Health importance affecting over 252 million people worldwide with Nigeria having a very high number of cases. It is caused by blood flukes of the genus Schistosoma and transmitted by freshwater snails. To achieve the current global elimination objectives, low-cost and easy-to-use diagnostic tools are critically needed. Recent innovations in optical and computer technologies have made handheld digital and smartphone-based microscopes a viable diagnostic approach. Development, validation and deployment of these diagnostic devices for field use, however, require the optimisation of its optical train for the registration of high-resolution images and the realisation of a robust system design that can be locally produced in low-income countries. Field research conducted in Nigeria with active involvement of key stakeholders in research and development (RD) led to the design of an initial prototype device for the diagnosis of urinary schistosomiasis, called Schistoscope 1.0. In this paper, we present further development of the Schistoscope 1.0 along two parallel design trajectories: A Raspberry Pi and a Smartphone-based Schistoscope. Specifically, we focused on the optimization of the optics, embodiment design and the electronics systems of the devices so as to produce a robust design with potential for local production.

@en

We present a simple method for the diagnosis of urinary schistosomiasis using an in-line lensless holographic microscope combined with flow cytometry technique. Using simple image processing algorithms and binary image classifier, our system provides automated detection of Schistosoma haematobium eggs in infected urine samples. Registered hologram is reconstructed by applying backpropagation from sensor to sample plane and reconstructed image is automatically analysed for the presence of S. haematobium eggs. Designed for use in a resource-poor laboratory setting, our proposed method has been implemented using a Raspberry Pi computer. From pre-clinical test performed with human urine samples spiked with S. haematobium eggs (approximately 200 eggs per 12 ml of urine), we achieved a sensitivity and specificity of 50.6% and 98.6% respectively. Our proposed method requires no complex sample preparation methods making the system simple to operate and useable in point-of-care diagnosis of urinary schistosomiasis.This method can be optimized to complement existing diagnostic procedures for the detection of S. haematobium eggs and can be deployed to inaccessible remote areas.@en

Fourier Ptychography is a computational imaging technique able to decouple high resolution from wide field of view, bypassing the diffraction limit of the microscope. Since it does not rely on high precision mechanics or fluorescent imaging, it is of practical interest for implementation in low scale devices. Despite its gains, realizing a functional low-cost setup working at the theoretical limits is challenging due to many factors causing discrepancies between theory and practice. Misalignment of the light emitting diode array (LED-array), optical system aberrations and use of partial coherent sources are common issues which have been addressed with calibration algorithms. However, physical interpretation of how these factors influence the algorithm and cause mismatches between theory and practice has had little attention so far. This work provides a discussion based on simulation results on the effect of the partial coherence of the source. From obtained results, an optimal set of LEDs for data acquisition is described which avoids degeneracy caused by partial coherence and is based on the numerical aperture (NA) of the objective and source parameters such as bandwidth and size.@en

Schistosomiasis is a neglected tropical disease of Public Health importance affecting over 252 million people worldwide with Nigeria having a very high number of cases. It is caused by blood flukes of the genus Schistosoma and transmitted by freshwater snails. To achieve the current global elimination objectives, low-cost and easy-to-use diagnostic tools are critically needed. Recent innovations in optical and computer technologies have made handheld digital and smartphone-based microscopes a viable diagnostic approach. Development, validation and deployment of these diagnostic devices for field use, however, require the optimisation of its optical train for the registration of high-resolution images and the realisation of a robust system design that can be locally produced in lowincome countries. Field research conducted in Nigeria with active involvement of key stakeholders in research and development (R&D) led to the design of an initial prototype device for the diagnosis of urinary schistosomiasis, called Schistoscope 1.0. In this paper, we present further development of the Schistoscope 1.0 along two parallel design trajectories: a Raspberry Pi and a Smartphone-based Schistoscope. Specifically, we focused on the optimization of the optics, embodiment design and the electronics systems of the devices so as to produce a robust design with potential for local production.@en

Inhomogeneities in the refractive index of a biological microscopy sample can introduce phase aberrations, severely impairing the quality of images. Adaptive optics can be employed to correct for phase aberrations and improve image quality. However, conventional adaptive optics can only correct a single phase aberration for the whole field of view (isoplanatic correction) while, due to the highly heterogeneous nature of biological tissues, the sample induced aberrations in microscopy often vary throughout the field of view (anisoplanatic aberration), limiting significantly the effectiveness of adaptive optics. This paper reports on a new approach for aberration correction in laser scanning confocal microscopy, in which a spatial light modulator is used to generate multiple excitation points in the sample to simultaneously scan different portions of the field of view with completely independent correction, achieving anisoplanatic compensation of sample induced aberrations, in a significantly shorter time compared to sequential isoplanatic correction of multiple image subregions. The method was tested in whole Drosophila brains and in larval Zebrafish, each showing a dramatic improvement in resolution and sharpness when compared to conventional isoplanatic adaptive optics.

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We consider the extension of the traditional projection-based phase retrieval algorithms by increasing the problem dimensionality and introducing novel projection operators. The approach is demonstrated on an example of phase retrieval for the high-NA case.

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In this Letter, we report on an algorithm and its implementation to reconstruct the wavefront as a continuous function from a bitmap image of the Hartmann–Shack pattern. The approach works with arbitrary raster geometry and does not require explicit spot definition and phase unwrapping. The system matrix, defining the coefficients of wavefront decomposition in the system of basis functions, is obtained as a result of a series of convolutions and thresholding operations on the reference and sample images.

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Schistosomiasis is a treatable and preventable neglected tropical disease of Public Health importance affecting over 250 million people worldwide while Nigeria is one of the high burden countries. Currently available diagnoses are cumbersome, low in sensitivity and not field-adaptable given the high skill required that are not available in the rural settings where the diseases are majorly prevalent. Democratizing access to diagnosis with a rapid, easy-to-use, accurate diagnosis is critical in currently stepped-up control, pre-elimination and elimination strategies for urinary schistosomiasis. In this paper, we describe the design process of a low-cost smartphone-based microscope for rapid diagnosis of urinary Schistosomiasis. Field research conducted in Nigeria with the active involvement of key stakeholders in the research and development (RD) process validated our assumptions and enabled the development of our proof-of-concept into a working prototype in three iterative designs steps. Through this design process, we investigated the local development of technical optics for good quality imaging and explored the simplification of sample preparation techniques using commonly available materials. Starting from the first iteration, the output of each design step was used as the input to the subsequent iterations to optimize our system design. Insightful results and input from the field demonstrated that an adaptive design approach was needed to facilitate the rapid development and deployment of point-of-care diagnostic devices for use in low-resource settings. It is our goal that these devices will be locally manufactured in Nigeria to expand access to the test given her huge population and high disease burden, quick repairs, and easy maintenance on the field.

@en

Schistosomiasis is a treatable and preventable neglected tropical disease of Public Health importance affecting over 250 million people worldwide while Nigeria is one of the high burden countries. Currently available diagnoses are cumbersome, low in sensitivity and not field-adaptable given the high skill required that are not available in the rural settings where the diseases are majorly prevalent. Democratizing access to diagnosis with a rapid, easy-to-use, accurate diagnosis is critical in currently stepped-up control, pre-elimination and elimination strategies for urinary schistosomiasis. In this paper, we describe the design process of a low-cost smartphone-based microscope for rapid diagnosis of urinary Schistosomiasis. Field research conducted in Nigeria with the active involvement of key stakeholders in the research and development (R&D) process validated our assumptions and enabled the development of our proof-of-concept into a working prototype in three iterative designs steps. Through this design process, we investigated the local development of technical optics for good quality imaging and explored the simplification of sample preparation techniques using commonly available materials. Starting from the first iteration, the output of each design step was used as the input to the subsequent iterations to optimize our system design. Insightful results and input from the field demonstrated that an adaptive design approach was needed to facilitate the rapid development and deployment of point-of-care diagnostic devices for use in low-resource settings. It is our goal that these devices will be locally manufactured in Nigeria to expand access to the test given her huge population and high disease burden, quick repairs,and easy maintenance on the field.@en