Huazhu Fu1, Mani Baskaran2, Yanwu Xu3, Stephen Lin4, Damon Wing Kee Wong5, Jiang Liu6, Tin A Tun7, Meenakshi Mahesh7, Shamira A Perera2, Tin Aung8. 1. 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. 2. Singapore Eye Research Institute, Singapore National Eye Center, Singapore; EYE-ACP, Duke-NUS Medical School, Singapore. 3. Institute for Infocomm Research, Agency for Science, Technology and Research, Singapore; Department of Artificial Intelligence Innovation Business, Baidu Inc., Beijing, China. 4. Microsoft Research, Beijing, China. 5. Singapore Eye Research Institute, Singapore National Eye Center, Singapore; Nanyang Technological University, Singapore. 6. Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Zhejiang, China; Department of Computer Science and Engineering, Southern University of Science and Technology, Guangzhou, China. 7. Singapore Eye Research Institute, Singapore National Eye Center, Singapore. 8. Singapore Eye Research Institute, Singapore National Eye Center, Singapore; EYE-ACP, Duke-NUS Medical School, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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.
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.
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