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人工智能在折射误差管理中的应用:系统性审查和元分析

Josephine Ampong¹, Sylvia Agyekum¹, Werner Eisenbarth²

¹Department of Optometry and Visual Science, College of Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

PLOS digital health

|September 25, 2025

概括

人工智能 (AI) 在诊断和预测折射误差 (RE) 中表现出高准确度. 需要进一步的研究来开发可泛化的人工智能模型,以便在眼科护理中更广泛地临床使用.

科学领域:

眼科和人工智能 (AI) 的研究

背景情况:

人工智能越来越多地被整合到医疗保健中,在眼科护理方面具有显著的潜力.
折射误差 (RE) 是一个重大的全球视觉挑战.
在RE管理中的AI应用包括诊断,检测,预测,进展监测和治疗.

研究的目的:

在诊断,检测,预测,进展和折射误差的治疗中系统地审查和元分析AI技术的有效性.
评估人工智能模型在管理RE的诊断和预测准确性.

主要方法:

按照PRISMA指南进行系统审查和元分析.
从创立到2025年1月,搜索了多个数据库 (PubMed,科学网,Embase,Scopus,Cochrane图书馆,谷歌学者) 从开始到2025年1月.
包括45项研究,19项正在进行元分析,利用深度学习 (DL) 和机器学习 (ML) 技术.
使用QUADAS-2评估了偏差风险,并使用R软件进行了元分析.

主要成果:

人工智能在检测/诊断方面表现出高的综合性能:灵敏度0.94,特异性0.96,DOR 382.56,SROC 0.98.
人工智能对RE的预测准确度很高:灵敏度为0.87,特异性为0.96,DOR为159.94,SROC为0.96.
进展和治疗的性能指标各不相同,AUC范围为0.60-0.99和MAE范围为0.119D-0.54D.

结论:

人工智能,特别是DL和ML,在折射误差管理中实现了高的诊断和预测准确性.
目前的人工智能应用显示出改善眼睛护理结果的前景.
未来的研究应该优先考虑开发可泛化AI模型,这些模型在各种数据集上进行训练,以便在临床上广泛采用.

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