Accessible, at-home detection of Parkinson’s disease via multi-task video analysis
Published in AAAI, 2025
We introduce a large-scale multi-task video dataset ($\mathbf{3,306}$ videos from $\mathbf{845}$ participants) and a novel fusion model, UFNet, for multimodal PD screening using webcam video. UFNet significantly outperformed single-task models in diagnostic accuracy (AUROC $\mathbf{\sim93\%}$), demonstrating scalable at-home detection.
Recommended citation: Islam, M.S., Adnan, T., Freyberg, J., Lee, S., Abdelkader, A., Pawlik, M., Schwartz, C., Jaffe, K., Schneider, R.B., Dorsey, R. and Hoque, E., 2025, April. Accessible, at-home detection of Parkinson’s disease via multi-task video analysis. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 27, pp. 28125-28133).
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