Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis.
Artificial intelligence for the detection of age-related macular degeneration in color fundus photographs: A systematic review and meta-analysis.
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DOI：
10.1016/j.eclinm.2021.100875
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发表时间：
2021-05
影响因子：
15.1
通讯作者：
Wei WB
中科院分区：
文献类型：
--
作者：
Dong L;Yang Q;Zhang RH;Wei WB
老年性黄斑变性(AMD)是老年人视力丧失的主要原因之一。人工智能(AI)的应用为AMD的诊断提供了便利。这项系统性综述和荟萃分析旨在量化人工智能在检测眼底照片中的AMD方面的表现。我们在2020年12月31日之前检索了PubMed、Embase、Web of Science和Cochrane Library，以查找有关人工智能在检测彩色眼底照片中AMD的应用的研究。然后，我们将数据汇集在一起进行分析。PROSPERO注册号：CRD42020197532。最终选择19篇文献进行系统综述，其中13篇纳入定量综合。所有研究均采用人类评分者作为参考标准。受试者工作特征曲线下的汇集面积为0.983(95%可信区间：0.979~0.987)。合并后的灵敏度、特异度和诊断优势比分别为0.88(95%CI：0.88~0.88)、0.9(95%CI：0.9~0.91)和275.27(95%CI：158.43~478.27)。进行了阈值分析，发现了潜在的阈值效应(Spearman相关系数：-0.600，P=0.030)，这是造成异质性的主要原因。在年龄相关眼病研究数据库中应用卷积神经网络的研究，合并的AUROC、灵敏度、特异度和DOR分别为0.983(95%CI：0.978-0.988)、0.88(95%CI：0.88-0.88)、0.91(95%CI：0.91-0.91)和273.14(95%CI：130.79-570.43)。我们的数据表明，人工智能能够在彩色眼底照片中检测到AMD。人工智能自动化工具的应用有利于AMD的诊断。首都卫生专项研究与发展(2020-1-2052)。
Age-related macular degeneration (AMD) is one of the leading causes of vision loss in the elderly population. The application of artificial intelligence (AI) provides convenience for the diagnosis of AMD. This systematic review and meta-analysis aimed to quantify the performance of AI in detecting AMD in fundus photographs. We searched PubMed, Embase, Web of Science and the Cochrane Library before December 31st, 2020 for studies reporting the application of AI in detecting AMD in color fundus photographs. Then, we pooled the data for analysis. PROSPERO registration number: CRD42020197532. 19 studies were finally selected for systematic review and 13 of them were included in the quantitative synthesis. All studies adopted human graders as reference standard. The pooled area under the receiver operating characteristic curve (AUROC) was 0.983 (95% confidence interval (CI):0.979–0.987). The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were 0.88 (95% CI:0.88–0.88), 0.90 (95% CI:0.90–0.91), and 275.27 (95% CI:158.43–478.27), respectively. Threshold analysis was performed and a potential threshold effect was detected among the studies (Spearman correlation coefficient: -0.600, P = 0.030), which was the main cause for the heterogeneity. For studies applying convolutional neural networks in the Age-Related Eye Disease Study database, the pooled AUROC, sensitivity, specificity, and DOR were 0.983 (95% CI:0.978–0.988), 0.88 (95% CI:0.88–0.88), 0.91 (95% CI:0.91–0.91), and 273.14 (95% CI:130.79–570.43), respectively. Our data indicated that AI was able to detect AMD in color fundus photographs. The application of AI-based automatic tools is beneficial for the diagnosis of AMD. Capital Health Research and Development of Special (2020–1–2052).
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作者：
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