In Purdue’s Weldon School of Biomedical Engineering, Professor Young L. Kim (University Faculty Scholar and Showalter Faculty Scholar) and his team are revolutionizing malaria screening with smartphone technology. Their latest research, published in npj Digital Medicine, introduces a simple yet powerful computer vision algorithm—using smartphone photography to analyze the inner eyelid for malaria risk assessment.

Malaria remains a major global health challenge often with asymptomatic infections in school-age children going undetected. While retinal imaging and microscopy imaging have advanced malaria detection, specialized equipment remains a barrier to widespread use. Professor Kim’s team addressed this gap in malaria-endemic regions, collaborating with the Rwanda Biomedical Center (part of the Ministry of Health) and the University of Rwanda. Their deep learning model analyzes radiomic features— spatial and textural information within the eyelid photo—to distinguish between malaria-infected and non-infected cases, achieving a predictive accuracy with an area under the receiver operating characteristic curve 0.68 to 0.84.

By leveraging the inner eyelid’s direct exposure to the microvasculature, this mobile health (mHealth) algorithm provides a noninvasive, accessible solution for malaria prescreening. The use of unmodified smartphone cameras eliminates the need for specialized medical devices, making early detection possible in resource-limited settings. With advanced machine learning and computer vision, this mHealth algorithm has the potential to transform malaria diagnosis and management worldwide. The team has recently launched the second phase of the study, equipping schoolteachers to serve as community health workers by collecting data while maintaining standard care for children in two districts in Rwanda. This initiative also exemplifies reciprocal innovation as the US is not malaria-free. This work is mainly supported by grants from that the National Institutes of Health’s Fogarty International Center.

Professor Kim, an expert in machine learning and biophotonics continues to push the boundaries of digital health. His lab explores machine learning, hyperspectral imaging and biosecurity innovations while developing scalable solutions that integrate technology with real-world healthcare challenges.

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