Early detection and management of SKIN-related negleCted tropical diseases using Artificial Intelligence in sub-saharan afRica (SkincAIr)
▶Summary
The SkincAIr project aims to develop an innovative AI-driven mobile application to support the early detection and of skin Neglected Tropical Diseases (skin NTDs) in Sub-Saharan Africa (SSA). NTDs significantly affect marginalized communities due to several factors, such as lack of trained healthcare staff and diagnostic tools. The project's objectives are: to improve the accuracy of Front-line Health Workers (FHW) of skin NTD identification, to create the largest public dataset of skin NTDs in the world (first in SSA), to reduce disease transmission through early diagnosis, to enhance real-time epidemiological surveillance, to enhance the knowledge of FHW, to develop novel AI models for skin disease monitoring, to ensure the digital solution is culturally tailored, to ensure compliance with clinical practices, ethical, legal aspects and participant rights, to ensure scalability and broad outreach of SkincAIr’s results, to advocate for greater awareness and policy support for NTDsSkincAIr will equip FHW with an app capable of detecting skin NTDs using advanced machine learning techniques while preserving privacy through geolocation features. The app will facilitate real-time epidemiological surveillance, contributing to improved disease mapping and hotspot identification. The project will be implemented in five SSA countries: Kenya, Senegal, Ethiopia, Nigeria, and the Democratic Republic of the Congo.Aligned with the Work Program, SkincAIr is anchored in the scope of Global Health EDCTP3 and regional strategies, targeting the highest demonstrated medical needs in SSA and addressing context-specific needs. It develops a solution with early-stage involvement of end users and implicated health services. The solution ensures seamless integration and interoperability, is sustainable, accessible, open-source, evidence-based, and compliant with data protection standards and global digital health public goods.