在ROC曲线决策值中实施净效益参数,用于AI驱动的乳房扫描查
Anastasia Petrovna Pamova1, Yuriy Aleksandrovich Vasilev1,2, Tatyana Mikhaylovna Bobrovskaya1
1Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow Health Care Department, Moscow, Russia.
Frontiers in big data
|December 4, 2025
概括
一种使用人工智能 (AI) 净效益分析的新方法在乳房镜中显著提高了诊断准确度. 这种方法减少了错误的阳性和阴性,提高了对更多女性的早期乳腺癌检测.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 生物统计学 生物统计学
背景情况:
- 人工智能 (AI) 在乳房造影中集成需要标准化的质量控制.
- 对于人工智能来说,在接收器操作特征 (ROC) 曲线上建立决策门是具有挑战性的.
- 目前的方法阻碍了在乳腺癌查中准确的AI性能评估.
研究的目的:
- 开发一种新的方法来确定人工智能在乳房摄影中的决策值.
- 优化AI性能,在查乳房影像中诊断乳房病理.
- 确保更广泛的女性人口得到及时的诊断.
主要方法:
- 在大型数字乳腺造影数据集 (663,606名患者) 上对三种人工智能模型的回顾性评估.
- 新的净收益 (NB) 分析以估计决策门.
- 使用McNemar的测试,比较NB衍生的值与Youden的指数.
主要成果:
- 通过NB方法,假阳性率降低了三倍,假阴性率降低了两倍.
- 真正阳性率通过NB方法翻了一番.
- 灵敏度从72% (尤登指数) 增加到99% (NB);特异性从75% (尤登指数) 降至48% (NB).
结论:
- 建议人工智能作为初始阅读器,使用新的NB值方法进行乳房镜中的双重阅读.
- 这种方法提高了AI的灵敏度,以改善乳腺癌诊断.
- 该方法支持及时准确地检测乳腺病理.
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