AI helps Stanford researchers map schistosomiasis risk in Senegal

Stanford researchers have developed an AI-powered system that combines field surveys, drones, and satellite imagery to identify schistosomiasis risk areas across Senegal.

The project began with fieldwork in Senegal, where researchers collected aquatic vegetation and snails from more than 30 river and estuary sites. The samples helped identify environmental conditions linked to schistosomiasis, which affects about 250 million people worldwide, mostly children in sub-Saharan Africa.

Professor Giulio De Leo of Stanford’s Doerr School of Sustainability said the research required scaling beyond local sampling. ‘The work was necessary to discover these risks, but we can only do so much locally.’

Early support from the Stanford Institute for Human-Centred AI enabled the development of machine learning tools capable of identifying disease-related snails and vegetation in imagery. The system now integrates field observations with drone and satellite data to detect potential infection hotspots.

Researchers say the approach can support public health monitoring and environmental analysis. The machine learning methods developed for the project are also being applied to agriculture, forest monitoring, and mosquito-borne disease research.

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