Automating the Diagnosis and Quantification of Urinary Schistosomiasis

Automating the Diagnosis and Quantification of Urinary Schistosomiasis

Jujjavarapu, Satyajith (TU Delft Electrical Engineering, Mathematics and Computer Science)

Vdovin, Gleb (mentor)
Agbana, T.E. (mentor)
Diehl, J.C. (graduation committee)
Smith, C.S. (graduation committee)

Delft University of Technology

Electrical Engineering | Embedded Systems

2020-02-27

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.

Embedded systems
Optics
Diagnostics
Image processing
Microscopy
Schistosomiasis

http://resolver.tudelft.nl/uuid:b213e477-343c-4a47-8d7c-8b931c15b3e9

2020-08-01

Student theses

master thesis

© 2020 Satyajith Jujjavarapu