Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images
P. Mehta, C. Petersen, J. C. Wen, M. R. Banitt, P. P. Chen, K. D. Bojikian, C. Egan, S.-I. Lee, M. Balazinska, A. Y. Lee, A. Rokem, The UK Biobank Eye and Vision Consortium
This paper develops an interpretable machine learning model for glaucoma detection using clinical data and multi-modal retinal images, achieving high accuracy and identifying both known and novel disease-related features.
American Journal of Ophthalmology · Nov 1, 2021Peer-reviewedBioengineering