AutoPtosis

TL;DR

AutoPtosis is an AI system that rapidly and accurately diagnoses eyelid drooping (ptosis) using interpretable models, reducing manual effort and improving clinical efficiency.

Contribution

The paper introduces AutoPtosis, a novel AI-based system with interpretable models for quick and accurate ptosis diagnosis, utilizing a diverse dataset and clinically inspired measurements.

Findings

Achieved 95.5% accuracy on verified data

Developed both deep learning and clinically inspired models

Reduces diagnosis time and healthcare burden

Abstract

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.