Innovative utilization of ultra-wide-field fundus images and deep-learning algorithms for screening high-risk posterior polar cataract.

Researchers

Journal

Modalities

Models

Abstract

Posterior capsular rupture (PCR) is a severe complication that occurs during cataract surgery. Patients with posterior polar cataract (PPC) are at a high risk of PCR. Mydriatic agents, despite notable side effects, enhance cataract posterior visibility during slit-lamp examination, aiding PPC identification. An ultra-wide-field (UWF) retinal imaging system, which does not need the aid of mydriatic agents, can visualize the projection (shadow) of a cataract onto the retina in fundus images. The relationship between PPC and the projected shadow remains unexplored. We hypothesized a cataract-shadow-projection theory and then validated it by developing a deep-learning algorithm which enables automatic and stable PPC screening using fundus images.Data were obtained from the Department of Ophthalmology at Far Eastern Memorial Hospital with permission from the hospital’s Institutional Review Board.Retrospective chart review data, including UWF fundus images.We developed a deep-learning algorithm to automatically detect PPC based on our cataract-shadow-projection theory. Retrospective data (n=546) with UWF fundus images were collected, and various model architectures and fields of view (FOVs) were tested for optimization.The final model achieved 80% overall accuracy, with 88.2% sensitivity and 93.4% specificity in PPC screening on a clinical validation dataset (n=103).This study established a significant relationship between PPC and the projected shadow, enabling surgeons to identify potential PPC risks preoperatively and reduce the incidence of posterior capsular rupture during cataract surgery.Copyright © 2024 Published by Wolters Kluwer on behalf of ASCRS and ESCRS.