将AI与PCR集成用于结核病诊断:评估用于胸部X射线的深度学习模型
Wei-Cheng Chiu1, Shan-Yueh Chang2, Chin Lin3,4,5,6
1Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical University, Taipei 114, Taiwan, China.
Bioengineering (Basel, Switzerland)
|December 30, 2025
概括
一个深度学习模型显示了使用胸部X射线 (CXR) 在低负荷区域进行结核病 (TB) 查的前景. 虽然有效,但人工智能辅助的CXR需要聚合酶链反应 (PCR) 确认才能准确诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 结核病 (TB) 构成了严重的全球健康威胁,需要高效的诊断工具.
- 胸部放射 (CXR) 是一种常见但非特异性的结核病查方法.
- 聚合酶链反应 (PCR) 测试对于确认结核病诊断至关重要.
研究的目的:
- 评估深度学习模型 (DLM) 通过CXR检测结核病的诊断性能.
- 为了比较DLM与PCR的准确性,结果在一个负担较低的地区.
- 评估DLM在资源有限的环境中的实用性.
主要方法:
- 来自两个医院的CXR图像和PCR发现的回顾性分析.
- 使用基于CheXzero视觉变压器的DLM,经过广泛的成像数据训练.
- 使用接收器操作特征 (ROC) 曲线,曲线下的面积 (AUC),灵敏度和特异性来评估性能.
主要成果:
- DLM获得了高的内部 (AUC 0.915) 和外部 (AUC 0.850) 验证分数.
- 该模型表现出良好的灵敏度和特异性,尽管准确性仅在PCR确认的病例中下降.
- 老年人和患有特定并发症的人的表现下降.
结论:
- 人工智能辅助的CXR查为结核病检测提供了潜力,特别是在资源有限的地区.
- 作为结核病查的支持工具,DLM表现有前途.
- 确认性PCR测试对于确定的结核病诊断至关重要.
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