  Blepharoptosis, or ptosis as it is more commonly referred to, is a condition
of the eyelid where the upper eyelid droops. The current diagnosis for ptosis
involves cumbersome manual measurements that are time-consuming and prone to
human error. In this paper, we present AutoPtosis, an artificial intelligence
based system with interpretable results for rapid diagnosis of ptosis. We
utilize a diverse dataset collected from the Illinois Ophthalmic Database Atlas
(I-ODA) to develop a robust deep learning model for prediction and also develop
a clinically inspired model that calculates the marginal reflex distance and
iris ratio. AutoPtosis achieved 95.5% accuracy on physician verified data that
had an equal class balance. The proposed algorithm can help in the rapid and
timely diagnosis of ptosis, significantly reduce the burden on the healthcare
system, and save the patients and clinics valuable resources.
