基于 fundus 照片的人工智能驱动的膜黑色素瘤和瘤之间的差异化:系统性审查和元分析
Theofilos Kanavos1,2,3, Effrosyni Birbas1,2,3,4, Jasmine H Francis5,6
1Northwell Health, New Hyde Park, NY, USA.
Translational vision science & technology
|January 27, 2026
概括
人工智能 (AI) 模型在使用 fundus 照片来区分阴道黑色素瘤和阴道神经时显示出高准确度. 这些人工智能工具对改善眼科临床决策充满希望.
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:
- 区分阴道黑色素瘤 (UM) 和阴道神经 (UN) 对于患者管理至关重要.
- 使用传统的诊断方法,这种区分往往是具有挑战性的.
- 机器学习 (ML),特别是深度学习 (DL),提供了一个潜在的解决方案.
研究的目的:
- 评估人工智能模型在UM与UN分类中的有效性.
- 使用基底照片分析ML算法的性能.
主要方法:
- 在2025年7月6日之前,对四个数据库进行系统的文献搜索.
- 包括从基金图像中开发UM/UN分类的ML模型的研究.
- 使用 QUADAS-2 评估偏差风险和适用性.
- 使用随机效应元分析汇集结果.
主要成果:
- 包括7项涉及6208名参与者的研究;六项使用DL,一个使用传统ML.
- 聚合性能:AUC 0.915,准确率为85.3%,灵敏度为83.7%,特异性为87.7%.
- 外部验证的模型显示AUC总值为0.873,表明保留了区分能力.
结论:
- 机器学习算法在通过基底摄影将UM与UN区分开来方面表现高且一致.
- 人工智能工具可以作为辅助工具,潜在地改善转诊和诊断信心.
- 需要对更大,多中心数据集和外部验证进行进一步的研究,以获得强大,可泛化的模型.
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