A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images

Huazhu Fu, Mani Baskaran, Yanwu Xu, Stephen Lin, Damon Wing Kee Wong, Jiang Liu, Tin A. Tun, Meenakshi Mahesh, Shamira A. Perera, Tin Aung

Research output: Journal PublicationArticlepeer-review

69 Citations (Scopus)

Abstract

Purpose: Anterior segment optical coherence tomography (AS-OCT) provides an objective imaging modality for visually identifying anterior segment structures. An automated detection system could assist ophthalmologists in interpreting AS-OCT images for the presence of angle closure. Design: Development of an artificial intelligence automated detection system for the presence of angle closure. Methods: A deep learning system for automated angle-closure detection in AS-OCT images was developed, and this was compared with another automated angle-closure detection system based on quantitative features. A total of 4135 Visante AS-OCT images from 2113 subjects (8270 anterior chamber angle images with 7375 open-angle and 895 angle-closure) were examined. The deep learning angle-closure detection system for a 2-class classification problem was tested by 5-fold cross-validation. The deep learning system and the automated angle-closure detection system based on quantitative features were evaluated against clinicians' grading of AS-OCT images as the reference standard. Results: The area under the receiver operating characteristic curve of the system using quantitative features was 0.90 (95% confidence interval [CI] 0.891–0.914) with a sensitivity of 0.79 ± 0.037 and a specificity of 0.87 ± 0.009, while the area under the receiver operating characteristic curve of the deep learning system was 0.96 (95% CI 0.953–0.968) with a sensitivity of 0.90 ± 0.02 and a specificity of 0.92 ± 0.008, against clinicians' grading of AS-OCT images as the reference standard. Conclusions: The results demonstrate the potential of the deep learning system for angle-closure detection in AS-OCT images.

Original languageEnglish
Pages (from-to)37-45
Number of pages9
JournalAmerican Journal of Ophthalmology
Volume203
DOIs
Publication statusPublished - Jul 2019
Externally publishedYes

ASJC Scopus subject areas

  • Ophthalmology

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