Development and validation of a convolutional neural network to identify blepharoptosis

Abstract

Blepharoptosis is a recognized cause of reversible vision loss and a non-specific indicator of neurological issues, occasionally heralding life-threatening conditions. Currently, diagnosis relies on human expertise and eyelid examination, with most existing Artificial Intelligence algorithms focusing on eyelid positioning under specialized settings. This study introduces a deep learning model with convolutional neural networks to detect blepharoptosis in more realistic conditions. Our model was trained and tested using high quality periocular images from patients with blepharoptosis as well as those with other eyelid conditions. The model achieved an area under the receiver operating characteristic curve of 0.918. For validation, we compared the model's performance against nine medical experts—oculoplastic surgeons, general ophthalmologists, and general practitioners—with varied expertise. When tested on a new dataset with varied image quality, the model's performance remained statistically comparable to that of human graders. Our findings underscore the potential to enhance telemedicine services for blepharoptosis detection.