检索结果-南通市图书馆

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

Advances in Ophthalmology Practice and Research 2024年第3期4卷 164-172页

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 Department of Ophthalmology Yong Loo Lin School of MedicineNational University of SingaporeSingapore Centre for Innovation and Precision Eye Health Yong Loo Lin School of MedicineNational University of SingaporeSingapore School of Chemistry Chemical Engineeringand BiotechnologyNanyang Technological UniversitySingapore Singapore Eye Research Institute Singapore National Eye CentreSingapore Department of Ophthalmology National University HospitalSingapore Ophthalmology and Visual Sciences(Eye ACP) Duke-NUS Medical SchoolSingapore

ackground:Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management ***,deep learning,a subset of Artificial Intelligence,has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical *** 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 *** review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive *** 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 *** included studies that had reported refractive error outcomes,regardless of publication *** 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 *** 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 *** 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 colle

关键词： Deep Learning Artificial Intelligence Refractive Error Retinal images Optical Coherence Tomography Photorefraction ocular images Prediction

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：