Auto Ptosis is a pipeline for detecting and calculating the features to help diagnosis ptosis. This disease presents itself with the “drooping” or “falling” of the upper eyelid and can lead to an impairment of visual field. We tackle this task through several different levels of hand-crafted design: (1) we train a classification network to differentiate healthy and ptosis eyes in an end-to-end fashion, (2) we use keypoint detection networks to delineate eyelids and pupils to derive the MRD1 distance (the marginal reflex distance-1, the gold standard of ptosis diagnosis), and (3) we also calculate supporting features such as percentage of iris visible through the eyelids. Features such as the latter were too difficult to calculate before, but with machine learning, can be an automatic output from any front-facing photograph. Calculating novel metrics like these can help expand clinical workflow and eliminate the need for complex clinical imaging set-ups, potentially supporting efforts in telemedicine.

Project Lead: Pete Setabutr

Team Lead: Darvin Yi

Team: Abdullah Aleem, Manoj Prabhakar Nallabothula