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Updated: Jul 3, 2025
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
Published on: May 25, 2020
Innovative utilization of ultra-wide field fundus images and deep learning algorithms for screening high-risk
Elsa L C Mai1, Bing-Hong Chen, Tai-Yuan Su
1From the Department of Electric Engineering, Yuan-Ze University, Taoyuan City, Taiwan (Mai, Chen, Su); Department of Ophthalmology, Far Eastern Memorial Hospital, Taiwan (Mai); Yuanpei University of Medical Technology, Hsinchu, Taiwan (Mai).
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.
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