In the production of green hydrogen, electrolyzers draw power from renewable energy sources. In this paper, the design of Solid State Transformer (SST) for large-scale H ₂ electrolyzers is benchmarked. The three most promising topologies are chosen for design and comparison, including Modular Multi-level Converter (MMC) based SST, Modular Multi-level Resonant (MMR) based SST, and Input-Series-Output-Parallel (ISOP) based SST. The distance between converter towers for insulation and maintenance, the insulation system of the transformer, and the cooling system are designed with practical considerations in order to have an accurate estimation of the volume and weight of the SST. Losses in the switches are calculated based on equations, and losses in passive components are calculated based on FEM simulation. The operating frequency for each topology is optimized to minimize loss, weight, and volume. The best of each topology is then compared with each other to identify the most suitable one for large-scale H ₂ electrolyzers.

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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

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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IntroductionThe INSPIRED project aims to develop inclusive Digital Optical Diagnostic Devices (DODDs) for schistosomiasis, to support disease management by enabling rapid diagnostic results, to improve efficient data management to guide decision-making and to provide healthcare workers with critical health information to facilitate follow-up action. Due to the non-availability of Target Product Profiles (TPPs) for guiding the development of digital diagnostics for schistosomiasis, we explored existing diagnostic TPPs.MethodsUsing a curated open access database (Notion database), we studied a selection of TPPs for diagnosing infectious diseases, focusing on specifications related to digital health products for Neglected Tropical Diseases (NTDs).ResultsEighteen TPPs originating from 12 documents, covering 13 specific diseases, were selected and their characteristics were labeled and entered into the database. Further exploration of the database revealed several gaps, including a lack of stakeholder input, sustainability, and TPP availability. Other significant gaps related to digital health platform interconnectivity and data stewardship specifically in relation to digital diagnostics, including DODDs.DiscussionThese findings reflect two possible scenarios: (1) there is currently no need for digital diagnostic devices for schistosomiasis and, by extension for other NTDs; or (2) those needs are not yet covered by TPPs. Therefore, we recommend that digital health diagnostics are included in the use cases for schistosomiasis control and elimination, at least in the ideal/desirable scenario, as this will guide research and incentivize investment in digital health diagnostics for schistosomiasis.@en

Diagnosis of soil-transmitted helminth (STH) and schistosome infections relies largely on conventional microscopy which has limited sensitivity, requires highly trained personnel and is error-prone. Rapid advances in miniaturization of optical systems, sensors and processors have enhanced research and development of digital and automated microscopes suitable for the detection of these diseases in resource-limited settings. While some studies have reported proof-of-principle results, others have evaluated the performance of working prototypes in field settings. The extensive commercialization of these innovative devices has, however, not yet been achieved. This review provides an overview of recent publications (2010-2022) on innovative field applicable optical devices which can be used for the diagnosis of STH and schistosome infections. Using an adapted technology readiness level (TRL) scale taking into account the WHO target product profile (TPP) for these diseases, the developmental stages of the devices were ranked to determine the readiness for practical applications in field settings. From the reviewed 18 articles, 19 innovative optical devices were identified and ranked. Almost all of the devices (85%) were ranked with a TRL score below 8 indicating that, most of the devices are not ready for commercialization and field use. The potential limitations of these innovative devices were discussed. We believe that the outcome of this review can guide the end-to-end development of automated digital microscopes aligned with the WHO TPP for the diagnosis of STH and schistosome infections in resource-limited settings.

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Conventional microscopy is the standard procedure for the diagnosis of schistosomiasis, despite its limited sensitivity, reliance on skilled personnel, and the fact that it is error prone. Here, we report the performance of the innovative (semi-)automated Schistoscope 5.0 for optical digital detection and quantification of Schistosoma haematobium eggs in urine, using conventional microscopy as the reference standard. At baseline, 487 participants in a rural setting in Nigeria were assessed, of which 166 (34.1%) tested S. haematobium positive by conventional microscopy. Captured images from the Schistoscope 5.0 were analyzed manually (semiautomation) and by an artificial intelligence (AI) algorithm (full automation). Semi- and fully automated digital microscopy showed comparable sensitivities of 80.1% (95% confidence interval [CI]: 73.2-86.0) and 87.3% (95%CI: 81.3-92.0), but a significant difference in specificity of 95.3% (95% CI: 92.4-97.4) and 48.9% (95% CI: 43.3-55.0), respectively. Overall, estimated egg counts of semi- and fully automated digital microscopy correlated significantly with the egg counts of conventional microscopy (r50.90 and r50.80, respectively, P < 0.001), although the fully automated procedure generally underestimated the higher egg counts. In 38 egg positive cases, an additional urine sample was examined 10 days after praziquantel treatment, showing a similar cure rate and egg reduction rate when comparing conventional microscopy with semiautomated digital microscopy. In this first extensive field evaluation, we found the semiautomated Schistoscope 5.0 to be a promising tool for the detection and monitoring of S. haematobium infection, although further improvement of the AI algorithm for full automation is required.

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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. @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