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

PURPOSE: To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images.

SETTING

Department of Ophthalmology, Far Eastern Memorial Hospital, New Taipei, Taiwan.

DESIGN

Retrospective chart review.

METHODS

A deep learning algorithm to automatically detect PPC was developed based on the cataract shadow projection theory. Retrospective data (n = 546) with ultra-wide field fundus images were collected, and various model architectures and fields of view were tested for optimization.

RESULTS

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).

CONCLUSIONS

This study established a significant relationship between PPC and the projected shadow, which may help surgeons to identify potential PPC risks preoperatively and reduce the incidence of posterior capsular rupture during cataract surgery.