Cao Jessica, Chang-Kit Brittany, Katsnelson Glen, Far Parsa Merhraban, Uleryk Elizabeth, Ogunbameru Adeteju, Miranda Rafael N, Felfeli Tina
Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada.
Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Diagn Progn Res. 2022 Jul 14;6(1):15. doi: 10.1186/s41512-022-00127-9.
With the rise of artificial intelligence (AI) in ophthalmology, the need to define its diagnostic accuracy is increasingly important. The review aims to elucidate the diagnostic accuracy of AI algorithms in screening for all ophthalmic conditions in patient care settings that involve digital imaging modalities, using the reference standard of human graders.
This is a systematic review and meta-analysis. A literature search will be conducted on Ovid MEDLINE, Ovid EMBASE, and Wiley Cochrane CENTRAL from January 1, 2000, to December 20, 2021. Studies will be selected via screening the titles and abstracts, followed by full-text screening. Articles that compare the results of AI-graded ophthalmic images with results from human graders as a reference standard will be included; articles that do not will be excluded. The systematic review software DistillerSR will be used to automate part of the screening process as an adjunct to human reviewers. After the full-text screening, data will be extracted from each study via the categories of study characteristics, patient information, AI methods, intervention, and outcomes. Risk of bias will be scored using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) by two trained independent reviewers. Disagreements at any step will be addressed by a third adjudicator. The study results will include summary receiver operating characteristic (sROC) curve plots as well as pooled sensitivity and specificity of artificial intelligence for detection of any ophthalmic conditions based on imaging modalities compared to the reference standard. Statistics will be calculated in the R statistical software.
This study will provide novel insights into the diagnostic accuracy of AI in new domains of ophthalmology that have not been previously studied. The protocol also outlines the use of an AI-based software to assist in article screening, which may serve as a reference for improving the efficiency and accuracy of future large systematic reviews.
PROSPERO, CRD42021274441.
随着人工智能（AI）在眼科领域的兴起，定义其诊断准确性的需求变得越来越重要。本综述旨在以人类分级员为参考标准，阐明AI算法在涉及数字成像模式的患者护理环境中筛查所有眼科疾病时的诊断准确性。
这是一项系统综述和荟萃分析。将在2000年1月1日至2021年12月20日期间对Ovid MEDLINE、Ovid EMBASE和Wiley Cochrane CENTRAL进行文献检索。通过筛选标题和摘要，然后进行全文筛选来选择研究。将纳入将AI分级的眼科图像结果与作为参考标准的人类分级员结果进行比较的文章；不进行比较的文章将被排除。系统综述软件DistillerSR将用于自动化部分筛选过程，作为人类评审员的辅助工具。全文筛选后，将通过研究特征、患者信息、AI方法、干预措施和结果等类别从每项研究中提取数据。将由两名经过培训的独立评审员使用诊断准确性研究质量评估（QUADAS-2）对偏倚风险进行评分。任何步骤中的分歧将由第三名裁决者解决。研究结果将包括汇总的接受者操作特征（sROC）曲线以及与参考标准相比，基于成像模式检测任何眼科疾病的人工智能的合并敏感性和特异性。将在R统计软件中进行统计计算。
本研究将为AI在眼科新领域的诊断准确性提供新的见解，这些领域以前尚未研究过。该方案还概述了使用基于AI的软件辅助文章筛选，这可能为提高未来大型系统综述的效率和准确性提供参考。
PROSPERO，CRD42021274441。
