Queen's University School of Medicine, Faculty of Health Sciences, Kingston, Ontario, Canada.
Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
BMJ Health Care Inform. 2023 Jul;30(1). doi: 10.1136/bmjhci-2023-100757.
Many efforts have been made to explore the potential of deep learning and artificial intelligence (AI) in disciplines such as medicine, including ophthalmology. This systematic review aims to evaluate the reporting quality of randomised controlled trials (RCTs) that evaluate AI technologies applied to ophthalmology.
A comprehensive search of three relevant databases (EMBASE, Medline, Cochrane) from 1 January 2010 to 5 February 2022 was conducted. The reporting quality of these papers was scored using the Consolidated Standards of Reporting Trials-Artificial Intelligence (CONSORT-AI) checklist and further risk of bias was assessed using the RoB-2 tool.
The initial search yielded 2973 citations from which 5 articles satisfied the inclusion/exclusion criteria. These articles featured AI technologies applied to diabetic retinopathy screening, ophthalmologic education, fungal keratitis detection and paediatric cataract diagnosis. None of the articles reported all items in the CONSORT-AI checklist. The overall mean CONSORT-AI score of the included RCTs was 53% (range 37%-78%). The individual scores of the articles were 37% (19/51), 39% (20), 49% (25), 61% (31) and 78% (40). All articles were scored as being moderate risk, or 'some concerns present', regarding potential risk of bias according to the RoB-2 tool.
A small number of RCTs have been published to date on the applications of AI in ophthalmology and vision science. Adherence to the 2020 CONSORT-AI reporting guidelines is suboptimal with notable reporting items often missed. Greater adherence will help facilitate reproducibility of AI research which can be a stimulus for more AI-based RCTs and clinical applications in ophthalmology.
许多努力都致力于探索深度学习和人工智能（AI）在医学等学科中的潜力，包括眼科学。本系统评价旨在评估评估 AI 技术在眼科学中应用的随机对照试验（RCT）的报告质量。
从 2010 年 1 月 1 日至 2022 年 2 月 5 日，对三个相关数据库（EMBASE、Medline、Cochrane）进行了全面检索。使用 CONSORT-AI 清单对这些论文的报告质量进行评分，并使用 RoB-2 工具进一步评估偏倚风险。
初步搜索产生了 2973 条引文，其中 5 篇文章符合纳入/排除标准。这些文章涉及应用于糖尿病视网膜病变筛查、眼科教育、真菌性角膜炎检测和儿童白内障诊断的 AI 技术。没有一篇文章报告了 CONSORT-AI 清单中的所有项目。纳入 RCT 的总体平均 CONSORT-AI 得分为 53%（范围 37%-78%）。文章的个人得分分别为 37%（19/51）、39%（20）、49%（25）、61%（31）和 78%（40）。根据 RoB-2 工具，所有文章均被评为中度风险，即“存在一些关注问题”，存在潜在偏倚风险。
迄今为止，已有少量 RCT 发表了关于 AI 在眼科学和视觉科学中的应用。2020 年 CONSORT-AI 报告指南的遵守情况并不理想，经常遗漏一些重要的报告项目。更高的依从性将有助于促进 AI 研究的可重复性，这可以刺激更多基于 AI 的 RCT 和在眼科学中的临床应用。
