Ocular image-based deep learning for predicting refractive error:A systematic review  

作　　者：Samantha Min Er Yew Yibing Chen Jocelyn Hui Lin Goh David Ziyou Chen Marcus Chun Jin Tan Ching-Yu Cheng Victor Teck Chang Koh Yih Chung Tham 

机构地区：[1]Department of Ophthalmology,Yong Loo Lin School of Medicine,National University of Singapore,Singapore [2]Centre for Innovation and Precision Eye Health,Yong Loo Lin School of Medicine,National University of Singapore,Singapore [3]School of Chemistry,Chemical Engineering,and Biotechnology,Nanyang Technological University,Singapore [4]Singapore Eye Research Institute,Singapore National Eye Centre,Singapore [5]Department of Ophthalmology,National University Hospital,Singapore [6]Ophthalmology and Visual Sciences(Eye ACP),Duke-NUS Medical School,Singapore

出　　处：《Advances in Ophthalmology Practice and Research》2024年第3期164-172,共9页眼科实践与研究新进展（英文）

摘　　要：ackground:Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies.Meanwhile,deep learning,a subset of Artificial Intelligence,has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise.Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques,a comprehensive systematic review on this topic is has yet be done.This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors.Main text:We search on three databases(PubMed,Scopus,Web of Science)up till June 2023,focusing on deep learning applications in detecting refractive error from ocular images.We included studies that had reported refractive error outcomes,regardless of publication years.We systematically extracted and evaluated the continuous outcomes(sphere,SE,cylinder)and categorical outcomes(myopia),ground truth measurements,ocular imaging modalities,deep learning models,and performance metrics,adhering to PRISMA guidelines.Nine studies were identified and categorised into three groups:retinal photo-based(n=5),OCT-based(n=1),and external ocular photo-based(n=3).For high myopia prediction,retinal photo-based models achieved AUC between 0.91 and 0.98,sensitivity levels between 85.10%and 97.80%,and specificity levels between 76.40%and 94.50%.For continuous prediction,retinal photo-based models reported MAE ranging from 0.31D to 2.19D,and R^(2) between 0.05 and 0.96.The OCT-based model achieved an AUC of 0.79–0.81,sensitivity of 82.30%and 87.20%and specificity of 61.70%–68.90%.For external ocular photo-based models,the AUC ranged from 0.91 to 0.99,sensitivity of 81.13%–84.00%and specificity of 74.00%–86.42%,MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60%to 96.70%.The reported papers collectively showed promising performances,in

关 键 词：Deep Learning Artificial Intelligence Refractive Error Retinal images OPTICAL Coherence Tomography PHOTOREFRACTION Ocular images PREDICTION 

分 类 号：R778[医药卫生—眼科]