Chenxi Zhang1, Feng He1, Bing Li1, Hao Wang2, Xixi He2, Xirong Li3,4, Weihong Yu5, Youxin Chen6. 1. Department of Ophthalmology, Key Laboratory of Ocular Fundus Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China. 2. Vistel AI Lab, Visionary Intelligence Ltd, Beijing, China. 3. Key Lab of Data Engineering and Knowledge Engineering, Renmin University of China, Beijing, China. 4. AI & Media Computing Lab, School of Information, Renmin University of China, Beijing, China. 5. Department of Ophthalmology, Key Laboratory of Ocular Fundus Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China. yuwh@pumch.cn. 6. Department of Ophthalmology, Key Laboratory of Ocular Fundus Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1# Shuai Fu Yuan, Dongcheng District, Beijing, 100730, China. chenyx@pumch.cn.
Abstract
PURPOSE: To investigate the detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus imaging system (Optos) with convolutional neural network technology. METHODS: This study included 1500 Optos color images for tessellated fundus confirmation and peripheral retinal lesion (lattice degeneration, retinal breaks, and retinal detachment) assessment. Three retinal specialists evaluated all images and proposed the reference standard when an agreement was achieved. Then, 722 images were used to train and verify a combined deep-learning system of 3 optimal binary classification models trained using seResNext50 algorithm with 2 preprocessing methods (original resizing and cropping), and a test set of 189 images were applied to verify the performance compared to the reference standard. RESULTS: With optimal preprocessing approach (original resizing method for lattice degeneration and retinal detachment, cropping method for retinal breaks), the combined deep-learning system exhibited an area under curve of 0.888, 0.953, and 1.000 for detection of lattice degeneration, retinal breaks, and retinal detachment respectively in tessellated eyes. The referral accuracy of this system was 79.8% compared to the reference standard. CONCLUSION: A deep-learning system is feasible to detect lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field images. And this system may be considered for screening and telemedicine.
PURPOSE: To investigate the detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus imaging system (Optos) with convolutional neural network technology. METHODS: This study included 1500 Optos color images for tessellated fundus confirmation and peripheral retinal lesion (lattice degeneration, retinal breaks, and retinal detachment) assessment. Three retinal specialists evaluated all images and proposed the reference standard when an agreement was achieved. Then, 722 images were used to train and verify a combined deep-learning system of 3 optimal binary classification models trained using seResNext50 algorithm with 2 preprocessing methods (original resizing and cropping), and a test set of 189 images were applied to verify the performance compared to the reference standard. RESULTS: With optimal preprocessing approach (original resizing method for lattice degeneration and retinal detachment, cropping method for retinal breaks), the combined deep-learning system exhibited an area under curve of 0.888, 0.953, and 1.000 for detection of lattice degeneration, retinal breaks, and retinal detachment respectively in tessellated eyes. The referral accuracy of this system was 79.8% compared to the reference standard. CONCLUSION: A deep-learning system is feasible to detect lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field images. And this system may be considered for screening and telemedicine.
Authors: Victor Tc Koh; Gerard Km Nah; Lan Chang; Adeline H X Yang; Sheng Tong Lin; Kyoko Ohno-Matsui; Tien Yin Wong; Seang Mei Saw Journal: Ann Acad Med Singapore Date: 2013-05 Impact factor: 2.473
Authors: Jing Cao; Kun You; Jingxin Zhou; Mingyu Xu; Peifang Xu; Lei Wen; Shengzhan Wang; Kai Jin; Lixia Lou; Yao Wang; Juan Ye Journal: EClinicalMedicine Date: 2022-09-05