在健康检查中利用可解释的人工智能 (XAI) 驱动的计算机辅助检测 (CAD) 系统对胸部X射线异常进行检测
Shizuka Nishii1, Katsuyuki Tomita2, Hirokazu Touge2
1Department of Respiratory Medicine, Hakuai Hospital, Yonago 683-0853, Japan.
Yonago acta medica
|August 14, 2025
概括
可解释的人工智能 (XAI) 驱动的计算机辅助检测 (CAD) 系统在检测胸部X射线异常方面显示出高精度和负预测值. 将XAI-CAD与专家审查相结合可能会提高未来查的准确性.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 胸部X射线 (CXR) 对于诊断各种疾病至关重要.
- 人工智能 (AI) 在医学成像中的整合正在迅速发展.
- 可解释AI (XAI) 旨在在AI决策中提供透明度.
研究的目的:
- 为了比较XAI驱动的计算机辅助检测 (CAD) 系统与非专家和肺部专家对CXRs异常发现的诊断性能.
- 评估XAI-CAD系统的准确性和负预测值 (NPV).
主要方法:
- 在一个单一的中心进行了一项回顾性研究.
- 分析了1,262张实施前和1,252张实施后的CXR图像.
- 测量了诊断准确度和NPV,最终的异常决定由两个肺病专家做出.
主要成果:
- 采用XAI驱动的CAD系统在检测CXR异常方面实现了0.84的精度和1.00的NPV.
- 对于结节阴影,XAI-CAD系统与肺病学专家相比没有劣势,精度为0.94和NPV为1.00.
- 该系统显示倾向于高估心脏扩大和胸膜加厚,导致对这些发现的敏感性降低.
结论:
- 基于XAI的CAD系统在改进CXR解释方面具有显著的潜力.
- 肺病专家和XAI-CAD系统的结合方法可能为未来的CXR查提供最高的准确性.
- 进一步的研究可能将重点放在完善系统对特定异常的灵敏度上.
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