Introduction
Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microscopists. An automated digital microscope with artificial intelligence (Schistoscope), offers a potential solution. This field study aimed to validate the diagnostic performance of the Schistoscope for detecting and quantifying Schistosoma haematobium eggs in urine compared to conventional microscopy and to a composite reference standard (CRS) consisting of real-time PCR and the up-converting particle (UCP) lateral flow (LF) test for the detection of schistosome circulating anodic antigen (CAA).

Methods
Based on a non-inferiority concept, the Schistoscope was evaluated in two parts: study A, consisting of 339 freshly collected urine samples and study B, consisting of 798 fresh urine samples that were also banked as slides for analysis with the Schistoscope. In both studies, the Schistoscope, conventional microscopy, real-time PCR and UCP-LF CAA were performed and samples with all the diagnostic test results were included in the analysis. All diagnostic procedures were performed in a laboratory located in a rural area of Gabon, endemic for S. haematobium.

Results
In study A and B, the Schistoscope demonstrated a sensitivity of 83.1% and 96.3% compared to conventional microscopy, and 62.9% and 78.0% compared to the CRS. The sensitivity of conventional microscopy in study A and B compared to the CRS was 61.9% and 75.2%, respectively, comparable to the Schistoscope. The specificity of the Schistoscope in study A (78.8%) was significantly lower than that of conventional microscopy (96.4%) based on the CRS but comparable in study B (90.9% and 98.0%, respectively).

Conclusion
Overall, the performance of the Schistoscope was non-inferior to conventional microscopy with a comparable sensitivity, although the specificity varied. The Schistoscope shows promising diagnostic accuracy, particularly for samples with moderate to higher infection intensities as well as for banked sample slides, highlighting the potential for retrospective analysis in resource-limited settings.@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

Designing new and inclusive diagnostic tools to detect Neglected Tropical Diseases (NTDs) to achieve rational disease control requires a co-design process where end-users’ input is important. Failure to involve all potential end-users in new diagnostics for NTDs can result in low use and adoption failure, leading to persistent infection hot spots and ineffective disease control. There are different categories of potential end-users of new diagnostic tools for NTD control, and it is unclear if there are differences between the user efficiency, effectiveness, perception, and acceptability across these end-user categories. This study evaluated the usability, user perception, contextual factors affecting the user’s experience, and acceptability of a new digital optical diagnostic device for NTDs across three types of potential end users. A total of 21 participants were tested. Laboratory scientists, technicians, and Community Health Extension Workers (CHEWs) in training achieved similar scores on the usability and user perception questionnaires with no statistically significant difference between end-user categories. All participants also have high scores for the user perception domains which strongly correlate with the acceptability of the AiDx NTDx Assist device. This study indicates that, by providing digital diagnostic tools in combination with minimal training and support, CHEWs undergoing training and, by extension, CHEWs post-training, can be involved in the diagnoses of NTDs, potentially enhancing a community’s capabilities to diagnose, treat, and control NTDs. @en

Urinary schistosomiasis is a waterborne parasitic infection caused by Schistosoma haematobium that affects approximately 30 million people annually in Nigeria. Treatment and eradication of this infection require effective diagnostics. However, current diagnostic tests have critical shortcomings and consequently are of limited value to stakeholders throughout the health care system who are involved in targeting the diagnosis and subsequent control of schistosomiasis. New diagnostic devices that fit the local health care infrastructure and support the different stakeholder diagnostic strategies remain a critical need. This study focuses on understanding, by means of Q-methodology, the context of use and application of a new diagnostic device that is needed to effectively diagnose urinary schistosomiasis in Oyo State, Nigeria. Q-methodology is a technique that investigates subjectivity by exploring how stakeholders rank-order opinion statements about a phenomenon. In this study, 40 statements were administered to evaluate stakeholder perspectives on the context of use and application of potential new diagnostic devices and how these perspectives or viewpoints are shared with other stakeholders. Potential new diagnostic devices will need to be deployable to remote or distant communities, be affordable, identify and confirm infection status before treatment in patients whose diagnosis of urinary schistosomiasis is based on self-reporting, and equip health care facilities with diagnostic devices optimized for the local setting while requiring local minimal infrastructural settings. Similarly, the context of use and application of a potential new diagnostic device for urinary schistosomiasis is primarily associated with the tasks stakeholders throughout the health care system perform or procedures employed. These findings will guide the development of new diagnostic devices for schistosomiasis that match the contextual landscape and diagnostic strategies in Oyo.@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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