Artificial Intelligence to Fight Against Bilharzia

Momy Seck of Station d'Innovation Aquacole in Senegal will apply AI approaches to remote sensing data to map spatiotemporal changes in the risk of the neglected tropical disease schistosomiasis and automatically generate public health bulletins supporting geographically-targeted control of the disease. Schistosomiasis, also called bilharzia, is caused by a parasitic worm transmitted by aquatic snails. The snails’ presence can be monitored by remote sensing by virtue of their association with a particular aquatic plant. They will use machine learning to better analyze the remote sensing data and generate schistosomiasis risk maps. They will then use Vision Language Models to extract information from these maps to be used by Large Language Models to automatically generate bulletins on risk. They will refine the bulletins through workshops with relevant public health authorities to assess their needs and the usefulness of the bulletins.