使用人工智能检测玻璃甲状腺疾病:系统性审查和元分析
Zahra Heidari1,2, Masoud Mirghorbani3, Mahdi Abounoori4
1Department of Ophthalmology, Bu-Ali Sina Hospital, Faculty of Medicine, Mazandaran University of Medical Sciences, Sari, Iran. zheidar1@lakeheadu.ca.
International ophthalmology
|January 27, 2026
概括
人工智能 (AI) 在从视网膜图像中检测视网膜疾病 (VRD) 中显示出高精度. 人工智能工具,特别是卷积神经网络 (CNN),为早期干预提供了有前途的诊断性能.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 早期检测玻璃甲状腺疾病 (VRDs) 对于预防视力丧失至关重要.
- 当前检测依赖于多式联络成像的手动解释.
- 人工智能 (AI) 为VRD早期检测和干预提供了一个有希望的方法.
研究的目的:
- 评估和总结人工智能模型在使用视网膜成像检测VRD的诊断性能.
- 在各种子组中评估AI在VRD检测中的准确性,灵敏性和特异性.
主要方法:
- 进行了系统的元分析,将研究记录在PROSPERO (CRD42023450207) 中.
- 在PubMed/MEDLINE,EMBASE和Web of Science进行了全面的文献搜索,截至2023年8月.
- 使用QUADAS-2工具评估了研究有效性,并将符合条件的文章分为九个VRD子组进行元分析.
主要成果:
- 包含了195项研究,产生了95.76%的整体聚合精度 (PEA) 估计.
- 聚合灵敏度 (PESen) 为91.94%,聚合特异性 (PESpe) 为96.09%.
- 人工智能模型,特别是卷积神经网络 (CNN),在各个子组中显示出高PEA (>90%).
结论:
- 人工智能诊断工具,特别是CNN,在检测VRD方面表现强.
- 在将有限的概括性研究结果应用于现实环境时,建议谨慎.
- 建议对新兴的人工智能模型进行进一步的研究,例如用于VRD检测的大型语言模型 (LLM).
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