将人工智能纳入扩散权重乳腺MRI有可能增加读者信心并减少工作负载
Dimitrios Bounias1,2, Lina Simons3, Michael Baumgartner1,4
1German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Heidelberg 69120, Germany.
Journal of the American Medical Informatics Association : JAMIA
|September 23, 2025
概括
人工智能 (AI) 在乳腺扩散加权成像 (DWI) 中的支持可以减少模两可的BI-RADS-like 3调用,并改善读者共识. 这种由人工智能驱动的系统有望提高乳腺癌查的诊断效率和准确性.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 乳腺扩散权重成像 (DWI) 是乳腺癌检测和乳腺密度较高的女性补充查的宝贵工具.
- 目前对乳腺DWI的解释可能是主观的,导致诊断准确度的变化和不必要的后续检查的可能性.
研究的目的:
- 评价一个人工智能 (AI) 驱动的计算机辅助诊断 (CAD) 系统对其对临床解释和减少乳腺DWI工作负载的影响.
- 评估人工智能系统在根据DWI对乳腺病变的分类方面的表现及其对读者之间的协议的影响.
主要方法:
- 一项回顾性研究,涉及824次考试用于模型开发和235次考试用于评估.
- 阅读是由三个读者进行的,有和没有AI-CAD的帮助,使用基于DWI的BI-RADS类别分类.
- 基于nnDetection的AI模型使用5倍交叉验证和组合进行了训练;使用AUC和评级者之间协议 (科恩的kappa) 评估了性能.
主要成果:
- 人工智能增强的方法显著减少了BI-RADS类3个调用,减少了29% (P=.019),并改善了评价者之间的协议 (0.57对0.49).
- 两位读者在AI-CAD的帮助下检测到更多恶性病变.
- 人工智能模型实现了0.78的AUC,在查年龄的女性中增加到0.82,表明在96%的灵敏度下减少20.9%的工作负载的潜力.
结论:
- 人工智能支持显示了通过减少模两可的分类和提高读者一致性来增强乳腺DWI解释的潜力.
- 人工智能-CAD系统展示了改进的诊断性能和效率,表明其在临床实践中的实用性.
- 建议对更大的研究队伍进行进一步的研究,以验证这些发现.
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