# AutoPtosis

arXiv: https://arxiv.org/abs/2106.03905 · HTML: https://mlsanity.com/paper/2106.03905/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 7 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Real-time Pupil Tracking from Monocular Video for Digital Puppetry](https://mlsanity.com/paper/2006.11341/) (2020)
- [I-ODA, Real-World Multi-modal Longitudinal Data for OphthalmicApplications](https://mlsanity.com/paper/2104.02609/) (2021)

## Abstract (arXiv)

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.