Abstract
Glaucoma is the second cause of blindness worldwide. Due to the complicated non-linear relationships between structural and functional assessment outcomes together with the large variabilities of disease progression patterns, accurate and reliable clinical assessment is a paramount importance for this slow but irreversible progressing disease. Recent advances in medical applications of artificial intelligence, especially deep learning (DL) approaches, have opened up unprecedented possibilities in computer aided clinical care. I will discuss a variety of DL studies on our large longitudinal glaucoma cohort data in collaborating IBM Watson Research Team. The topics include: 1) estimation of visual field parameters out of raw 3D optical coherence tomography (OCT) image data using a feature agnostic data driven approach, 2) forecasting functional measurements out of clinically available demographic information augmented by OCT feature analysis, 3) generating future 2D biomarker color mapping on OCT image data, and 4) identifying novel biomarkers and exploration of structure-function relationships using a group class activation mapping technique.
Funding: NIH R01-EY013178, NIH R01-EY030929, P30EY013079 (unrestricted grant by the Research to Prevent Blindness).