Infectious diseases such as malaria and neglected tropical diseases (NTDs) are debilitating and deadly, affecting more than one billion people. Optical microscopy remains the primary diagnostic tool; however, its limitations—such as time-consuming processes and reliance on trained microscopists, particularly in resource-constrained settings—can be addressed with the support of AI-powered diagnostic systems.

We propose a holistic approach, focusing on the sample rather than the disease itself. The system is easy to use and works offline. It adapts to various contexts, ensuring broad applicability and scalability.

 Lin L, et al. (2024) Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy. PLoS Negl Trop Dis 18(4): e0012117

Dacal, E. et al. “Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection.” PLoS neglected tropical diseases 15.9 (2021): e0009677.

Mancebo-Martin, R. et al. “How many labels do I need? Self-supervised learning strategies for multiple blood parasites classification in microscopy images.” medRxiv (2024): 2024-02.