人工智能在视网膜前膜护理中的作用：综述

IF 3.2 Q1 OPHTHALMOLOGY

Ophthalmology science Pub Date : 2024-12-20 DOI:10.1016/j.xops.2024.100689

David Mikhail MD(C), MSc(C) , Daniel Milad MD , Fares Antaki MD, CM , Karim Hammamji MD , Cynthia X. Qian MD , Flavio A. Rezende MD, PhD , Renaud Duval MD, FRCSC

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引用次数: 0

摘要

在眼科领域，人工智能（AI）展示了在不同疾病中使用眼科成像的潜力，通常与眼科医生的表现相匹配。然而，用于视网膜前膜（ERM）管理的机器学习模型的范围在方法、应用和性能上都有所不同，在很大程度上仍未得到综合。视网膜上膜的管理依赖于临床评估和影像学，在严重损害的情况下考虑手术干预。人工智能对眼科图像和临床特征的分析可以增强ERM的检测、表征和预测，有可能改善临床决策。本综述旨在评估人工智能模型在ERM诊断、表征和预测方面的方法、应用和报告性能。方法对Ovid MEDLINE、EMBASE、Cochrane Central Register of Controlled Trials、Cochrane Database of Systematic Reviews、Web of Science Core Collection 5个电子数据库进行全面的文献检索，检索时间自成立至2024年11月14日。包括与ERM背景下的人工智能算法有关的研究。衡量的主要结果将是报告的设计、ERM管理中的应用以及每个人工智能模型的性能。结果共检索到390篇文献，其中33篇符合纳入标准。有30项研究（91%）报告了他们的培训和验证方法。总共包括61种不同的人工智能模型。OCT扫描和眼底照片分别用于26篇（79%）和7篇（21%）论文。有监督学习和有监督和无监督学习分别用于32（97%）和1（3%）项研究。27项研究（82%）开发或改编了使用图像的人工智能模型，而5项（15%）的模型同时使用图像和临床特征，1项（3%）使用术前和术后临床特征而不使用眼科图像。研究目标分为三个阶段的ERM护理。23项研究（70%）将人工智能用于诊断（第1阶段），1项研究（3%）确定了ERM特征（第2阶段），6项研究（18%）预测了诊断后的视力损害或术后视力结果（第3阶段）。没有研究治疗计划的文章。三项研究（9%）在第一阶段和第二阶段使用人工智能。在将人工智能的表现与人类评分者（即视网膜专家、普通眼科医生和实习生）进行比较的16项研究中，有10项（63%）报告了同等或更高的表现。结论人工智能驱动的眼科图像和临床特征评估在检测ERM、识别其形态特征和预测ERM手术后的视觉结果方面表现优异。未来的研究可能会考虑验证算法在个人治疗计划制定中的临床应用，理想情况下，确定可能从手术中获益最多的患者。财务披露作者在本文中讨论的任何材料中没有专有或商业利益。

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The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review

Topic

In ophthalmology, artificial intelligence (AI) demonstrates potential in using ophthalmic imaging across diverse diseases, often matching ophthalmologists' performance. However, the range of machine learning models for epiretinal membrane (ERM) management, which differ in methodology, application, and performance, remains largely unsynthesized.

Clinical Relevance

Epiretinal membrane management relies on clinical evaluation and imaging, with surgical intervention considered in cases of significant impairment. AI analysis of ophthalmic images and clinical features could enhance ERM detection, characterization, and prognostication, potentially improving clinical decision-making. This scoping review aims to evaluate the methodologies, applications, and reported performance of AI models in ERM diagnosis, characterization, and prognostication.

Methods

A comprehensive literature search was conducted across 5 electronic databases including Ovid MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science Core Collection from inception to November 14, 2024. Studies pertaining to AI algorithms in the context of ERM were included. The primary outcomes measured will be the reported design, application in ERM management, and performance of each AI model.

Results

Three hundred ninety articles were retrieved, with 33 studies meeting inclusion criteria. There were 30 studies (91%) reporting their training and validation methods. Altogether, 61 distinct AI models were included. OCT scans and fundus photographs were used in 26 (79%) and 7 (21%) papers, respectively. Supervised learning and both supervised and unsupervised learning were used in 32 (97%) and 1 (3%) studies, respectively. Twenty-seven studies (82%) developed or adapted AI models using images, whereas 5 (15%) had models using both images and clinical features, and 1 (3%) used preoperative and postoperative clinical features without ophthalmic images. Study objectives were categorized into 3 stages of ERM care. Twenty-three studies (70%) implemented AI for diagnosis (stage 1), 1 (3%) identified ERM characteristics (stage 2), and 6 (18%) predicted vision impairment after diagnosis or postoperative vision outcomes (stage 3). No articles studied treatment planning. Three studies (9%) used AI in stages 1 and 2. Of the 16 studies comparing AI performance to human graders (i.e., retinal specialists, general ophthalmologists, and trainees), 10 (63%) reported equivalent or higher performance.

Conclusion

Artificial intelligence–driven assessments of ophthalmic images and clinical features demonstrated high performance in detecting ERM, identifying its morphological properties, and predicting visual outcomes following ERM surgery. Future research might consider the validation of algorithms for clinical applications in personal treatment plan development, ideally to identify patients who might benefit most from surgery.