人工智能用于子宫内膜癌诊断测试的准确性：系统评价与荟萃分析。
Diagnosis Test Accuracy of Artificial Intelligence for Endometrial Cancer: Systematic Review and Meta-Analysis.
作者信息
Wang Longyun, Wang Zeyu, Zhao Bowei, Wang Kai, Zheng Jingying, Zhao Lijing
机构信息
Department of Rehabilitation, School of Nursing, Jilin University, Changchun, China.
Department of Gynecology and Obstetrics, The Second Hospital of Jilin University, Changchun, China.
出版信息
J Med Internet Res. 2025 Apr 18;27:e66530. doi: 10.2196/66530.
BACKGROUND
Endometrial cancer is one of the most common gynecological tumors, and early screening and diagnosis are crucial for its treatment. Research on the application of artificial intelligence (AI) in the diagnosis of endometrial cancer is increasing, but there is currently no comprehensive meta-analysis to evaluate the diagnostic accuracy of AI in screening for endometrial cancer.
OBJECTIVE
This paper presents a systematic review of AI-based endometrial cancer screening, which is needed to clarify its diagnostic accuracy and provide evidence for the application of AI technology in screening for endometrial cancer.
METHODS
A search was conducted across PubMed, Embase, Cochrane Library, Web of Science, and Scopus databases to include studies published in English, which evaluated the performance of AI in endometrial cancer screening. A total of 2 independent reviewers screened the titles and abstracts, and the quality of the selected studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. The certainty of the diagnostic test evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system.
RESULTS
A total of 13 studies were included, and the hierarchical summary receiver operating characteristic model used for the meta-analysis showed that the overall sensitivity of AI-based endometrial cancer screening was 86% (95% CI 79%-90%) and specificity was 92% (95% CI 87%-95%). Subgroup analysis revealed similar results across AI type, study region, publication year, and study type, but the overall quality of evidence was low.
CONCLUSIONS
AI-based endometrial cancer screening can effectively detect patients with endometrial cancer, but large-scale population studies are needed in the future to further clarify the diagnostic accuracy of AI in screening for endometrial cancer.
TRIAL REGISTRATION
PROSPERO CRD42024519835; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024519835.
背景
子宫内膜癌是最常见的妇科肿瘤之一，早期筛查和诊断对其治疗至关重要。人工智能（AI）在子宫内膜癌诊断中的应用研究日益增多，但目前尚无全面的荟萃分析来评估AI在子宫内膜癌筛查中的诊断准确性。
目的
本文对基于AI的子宫内膜癌筛查进行系统评价，以明确其诊断准确性，并为AI技术在子宫内膜癌筛查中的应用提供证据。
方法
在PubMed、Embase、Cochrane图书馆、Web of Science和Scopus数据库中进行检索，纳入以英文发表的评估AI在子宫内膜癌筛查中性能的研究。共有2名独立评审员筛选标题和摘要，并使用诊断准确性研究质量评估-2（QUADAS-2）工具评估所选研究的质量。使用推荐分级评估、制定和评价（GRADE）系统评估诊断试验证据的确定性。
结果
共纳入13项研究，用于荟萃分析的分层汇总接受者操作特征模型显示，基于AI的子宫内膜癌筛查的总体敏感性为86%（95%CI 79%-90%），特异性为92%（95%CI 87%-95%）。亚组分析显示，在AI类型、研究地区、发表年份和研究类型方面结果相似，但证据的总体质量较低。
结论
基于AI的子宫内膜癌筛查可有效检测子宫内膜癌患者，但未来需要进行大规模人群研究，以进一步明确AI在子宫内膜癌筛查中的诊断准确性。
试验注册
PROSPERO CRD42024519835；https://www.crd.york.ac.uk/PROSPERO/view/CRD42024519835 。
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