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.

Schistosomiasis is a neglected tropical disease thatis predominantly diagnosed by conventional microscopy in Sub-Saharan Africa. However, effective diagnosis by conventional microscopy is limited by multiple technical and logistic barriers.Alternative diagnostic techniques are needed. The Schistoscope is a digital optical device that has been designed to support microscopists for the detection of schistosomiasis in endemic resource-limited settings. Aim: A user-centered design approachwas used to assess the usability and user-acceptance of the Schistoscope compared to conventional microscopy in the Federal Capital Territory, Abuja, Nigeria. In this study, usability and acceptance are defined as being easy-to-use, efficient, and suitable in the daily workflow by end-users. Methods: Using a qualitative conventional context analysis approach, a mixedmethods questionnaire was used to elucidate themes related to the usability and user-acceptance of the device. Participants included trained microscopists and university students (n=17). Results: Participants answered both ranked and open questions. Overall the device’s use was considered to be easy and acceptable
in the routine workflow of a microscopist. The auto-scan feature was considered to have added value. Critical feedback regarding aesthetics of the device, particularly related to size, was noted by the participants. Conclusion: The usability approach used in this study elucidated valuable insights of end-users. The Schistoscope was very well perceived by both medical students and trained microscopists. Critical feedback will be used to further improve the next iterative design of the device.

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.