Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada.
Faculty of Science, The University of Western Ontario, London, ON, Canada.
Eye (Lond). 2022 May;36(5):994-1004. doi: 10.1038/s41433-021-01540-y. Epub 2021 May 6.
The objective of this study was to systematically review and meta-analyze the diagnostic accuracy of current machine learning classifiers for age-related macular degeneration (AMD). Artificial intelligence diagnostic algorithms can automatically detect and diagnose AMD through training data from large sets of fundus or OCT images. The use of AI algorithms is a powerful tool, and it is a method of obtaining a cost-effective, simple, and fast diagnosis of AMD.
MEDLINE, EMBASE, CINAHL, and ProQuest Dissertations and Theses were searched systematically and thoroughly. Conferences held through Association for Research in Vision and Ophthalmology, American Academy of Ophthalmology, and Canadian Society of Ophthalmology were searched. Studies were screened using Covidence software and data on sensitivity, specificity and area under curve were extracted from the included studies. STATA 15.0 was used to conduct the meta-analysis.
Our search strategy identified 307 records from online databases and 174 records from gray literature. Total of 13 records, 64,798 subjects (and 612,429 images), were used for the quantitative analysis. The pooled estimate for sensitivity was 0.918 [95% CI: 0.678, 0.98] and specificity was 0.888 [95% CI: 0.578, 0.98] for AMD screening using machine learning classifiers. The relative odds of a positive screen test in AMD cases were 89.74 [95% CI: 3.05-2641.59] times more likely than a negative screen test in non-AMD cases. The positive likelihood ratio was 8.22 [95% CI: 1.52-44.48] and the negative likelihood ratio was 0.09 [95% CI: 0.02-0.52].
The included studies show promising results for the diagnostic accuracy of the machine learning classifiers for AMD and its implementation in clinical settings.
本研究旨在系统回顾和荟萃分析当前机器学习分类器在年龄相关性黄斑变性（AMD）中的诊断准确性。人工智能诊断算法可以通过来自大量眼底或 OCT 图像的训练数据自动检测和诊断 AMD。使用 AI 算法是一种强大的工具，是获得具有成本效益、简单、快速的 AMD 诊断的方法。
系统地、彻底地检索了 MEDLINE、EMBASE、CINAHL 和 ProQuest Dissertations and Theses，并检索了通过美国视觉研究协会、美国眼科学会和加拿大眼科学会举办的会议。使用 Covidence 软件筛选研究，并从纳入研究中提取敏感性、特异性和曲线下面积的数据。使用 STATA 15.0 进行荟萃分析。
我们的搜索策略从在线数据库中确定了 307 条记录，从灰色文献中确定了 174 条记录。共有 13 条记录，64798 例受试者（和 612429 张图像）用于定量分析。使用机器学习分类器进行 AMD 筛查的汇总估计敏感性为 0.918 [95%CI：0.678，0.98]，特异性为 0.888 [95%CI：0.578，0.98]。AMD 病例的阳性筛查试验的相对几率是无 AMD 病例的阴性筛查试验的 89.74 倍[95%CI：3.05-2641.59]。阳性似然比为 8.22 [95%CI：1.52-44.48]，阴性似然比为 0.09 [95%CI：0.02-0.52]。
纳入的研究显示出机器学习分类器对 AMD 的诊断准确性及其在临床环境中的应用有很好的结果。
