Automating the Diagnosis and Quantification of Urinary Schistosomiasis

Schistosomiasis is a neglected tropical disease (NTD) that affects around a quarter-billion people worldwide. Most of the infected people live in tropical regions, especially in sub-Saharan Africa, where there is limited access to diagnostic and other relevant medical facilities. The current reference standard diagnostic procedure - conventional microscopy - is a relatively inexpensive procedure to use on a large scale, but it still requires trained operators and an initial financial investment which are hard to procure and maintain in remote areas with inadequate facilities. Moreover, the diagnostic sensitivity of the procedure is modest and varies over a wide range. In this work, we present the development of an inexpensive diagnostic instrument for urinary schistosomiasis that is automated to scan, analyse and diagnose the disease, and quantify the level of infection. We explore the design of the device with an open-source philosophy in mind, to enable makerspaces and other interested parties to reproduce the device locally. The device is manufacturable for as little as €200. It adheres to the standard sample preparation and diagnosis procedure established by the World Health Organisation (WHO), and images the relevant biomarkers to an adequate resolution for automated detection, diagnosis, and quantification for epidemiological surveillance. We believe this device can be an essential means for point-of-care diagnosis in resource-limited settings.Electrical Engineer | Embedded System

Schistoscope: smartphone versus Raspberry Pi based low-cost diagnostic device for urinary Schistosomiasis

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 (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