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

Carel Diehl, Jan, Oyibo, Prosper, Agbana, Temitope, Jujjavarapu, Satyajith, Van, G-Young, Vdovin, Gleb and Oyibo, Wellington 2021. Schistoscope: smartphone versus Raspberry Pi based low-cost diagnostic device for urinary Schistosomiasis. Presented at: 2020 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, 29 October - 1 November 2020. Proceedings of 2020 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 10.1109/ghtc46280.2020.9342871

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Publisher:	IEEE
ISBN:	978-1-7281-7389-4
Last Modified:	08 Dec 2023 14:30
URI:	https://orca.cardiff.ac.uk/id/eprint/163953

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