使用人工智能系统在CT扫描中检测活跃结核病在高患病率环境中:一项多中心研究.
Qian Wang1, Zhongfa Zhang2, Li Xia3
1Tuberculosis Prevention and Control Center, Shandong Center for Disease Control and Prevention, Jinan, Shandong, China.
Scientific reports
|November 11, 2025
概括
人工智能系统可以有效地识别活跃结核病 (ATB) 使用CT扫描在专门医院. 这项技术有望改善高患病率地区的诊断和资源管理.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- 结核病 (TB) 仍然是一个全球性卫生挑战,特别是在高患病率的环境中.
- 准确及时诊断活跃结核病 (ATB) 对于有效的治疗和控制至关重要.
- CT成像是一种有价值的工具,但解释可能具有挑战性,特别是在资源有限或压倒性的环境中.
研究的目的:
- 评估人工智能 (AI) 系统在CT图像中识别活跃结核病 (ATB) 的可行性和通用性.
- 评估AI系统在高患病率地区的结核病专科医院的性能.
- 为实施人工智能提供见解,以支持结核病诊断中的临床决策.
主要方法:
- 使用来自三家结核病专业医院的1741张CT图像的多中心数据集对人工智能系统的回顾性验证.
- 数据集包括ATB病例,肺炎,肺结节和正常对照.
- 在四个应用场景中评估了系统效用和通用性,并对医院之间的性能进行了对对比.
主要成果:
- 人工智能系统在三个参与医院中表现出良好的通用性.
- 达到高的曲线下面面积 (AUC) 值:>0.9 (异常与正常),>0.95 (ATB与正常),>0.8 (ATB与非ATB).
- 区分ATB与其他异常 (肺炎,结节) 的AUC在0.762到0.906之间,在几个比较中,医院之间没有显著的性能差异.
结论:
- 在结核病专业医院中,使用人工智能系统在CT图像中识别ATB是可行的.
- 人工智能系统显示了支持临床决策的潜力,并优化了结核病负担重重的医疗保健机构的资源配置.
- 这项研究为在管理结核病病例时实施人工智能工具提供了有价值的数据.
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