目的 采用Meta分析的方法系统评价人工智能辅助诊断系统在糖尿病视网膜病变（DR）诊断中的应用价值。方法 计算机检索PubMed、Web of Science、Embase、Cochrane Library、中国生物医学文献数据库（CBM）、中国知网（CNKI）、万方数据知识服务平台（WanFang Data）及维普数据库（VIP）等数据库，获取2019年1月至2024年9月关于人工智能辅助诊断系统对DR诊断价值的相关文献。采用QUADAS-2评价工具对纳入研究进行质量评估，采用 Stata 17.0、Meta Disc 1.4软件对纳入的文献进行Meta分析。结果 本研究共纳入23篇文献，Meta分析结果显示，人工智能辅助诊断系统在诊断DR时合并灵敏度、合并特异度、合并阳性似然比、合并阴性似然比及合并诊断比值比分别为 0.92（95%CI: 0.89，0.94）、0.94（95%CI: 0.91，0.96）、15.6（95%CI: 10.6，22.9）、0.09（95%CI: 0.07，0.12）及174（95%CI: 112，271），受试者工作曲线下面积（area under the ROC curve,AUC）为 0.97（95%CI: 0.96，0.98）。Meta回归与亚组分析显示研究的异质性来源于研究类型、患者类型、患者来源及AI算法类型。Deek＇s 漏斗图检验结果提示未发现明显的发表偏倚( P= 0. 15＞0. 05)，表明合并结果较为稳健。结论 人工智能辅助诊断系统对DR具有较高的诊断价值，可在DR早期筛查与诊断中推广应用。

AIM To evaluate the application value of artificial intelligence-assisted systems in diagnosing diabetic retinopathy (DR) by Meta-analysis. Methods PubMed, Web of Science, Embase, Cochrane Library, CBM, CNKI, WanFang Data and VIP database were searched to collect relevant literature on the diagnostic value of artificial intelligence-assisted systems for DR from January 2019 to September 2024. The QUADAS-2 tool was used to evaluate the quality of the included studies, and meta-analysis was performed using Stata 17.0 and Meta Disc 1.4 software. Results ?A total of 23 studies were included. The results of meta-analysis showed that the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under the SROC curve (AUC) were 0.92 (95%CI: 0.89, 0.94), 0.94 (95%CI: 0.91, 0.96), 15.6 (95%CI: 10.6, 22.9), 0.09 (95%CI: 0.07, 0.12), 174 (95%CI: 112, 271) and 0.97(95%CI: 0.96，0.98).Meta-regression and subgroup analyses indicated that the heterogeneity of the studies originated from study type, patient type, patient source, and AI algorithm type. Deeks’ funnel plot test suggested no significant publication bias (P = 0.15 > 0.05), indicating that the results were robust. Conclusions The artificial intelligence-assisted system demonstrates high diagnostic value for DR and can be widely implemented in the early screening and diagnosis of DR.