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Abstract #81618 Published in IGR 20-4
A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images
Fu H; Baskaran M; Xu Y; Lin S; Wong DWK; Liu J; Tun TA; Mahesh M; Perera SA; Aung TAmerican Journal of Ophthalmology 2019; 203: 37-45
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.
Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhejiang, China; Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates; Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore. Electronic address: huazhufu@gmail.com.
Full articleClassification:
6.9.2.1 Anterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)
6.9.5 Other (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis)
