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人工智能的未来多站点验证,以检测结核病和胸部X射线异常
Sahar Kazemzadeh1, Atilla P Kiraly1, Zaid Nabulsi1
1Google, Mountain View, CA, USA.
NEJM AI
|January 17, 2025
概括
解释胸部X射线 (CXR) 的人工智能 (AI) 在高负担人群中对活跃肺结核 (TB) 的分类方面表现出对放射科医生的非劣势. 虽然不符合世界卫生组织 (WHO) 的所有目标,但人工智能为结核病检测和识别其他CXR异常提供了有希望的工具.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 传染性疾病 传染性疾病
背景情况:
- 人工智能 (AI) 具有解释胸部X射线 (CXR) 的潜力,以支持可访问的活跃肺结核 (TB) 选.
- 这在资源有限的环境中尤其重要,因为结核病的流行率很高.
研究的目的:
- 评估两个基于云的CXR人工智能系统用于结核病检测和一般异常检测的性能.
- 将AI性能与放射科医生和世界卫生组织 (WHO) 目标在高结核病和艾滋病毒负担的人群中进行比较.
主要方法:
- 评估了两个AI系统:一个用于结核病检测 (TBAI),另一个用于一般CXR异常 (异常AI).
- 通过将人工智能结果与放射科医生的解释和世卫组织目标 (90%的灵敏度,70%的特异性) 进行比较,对1978年患有结核病症状,结核病接触者或新确诊的艾滋病毒的成年人进行了评估.
- 对放射科医生和预定义的灵敏度/特异性目标的AI性能进行了分析.
主要成果:
- 结核病AI在高灵敏度值显示了87%的灵敏度和70%的特异性,在平衡值显示了78%的灵敏度和82%的特异性.
- 结核病AI在高灵敏度值时与放射科医生的平均灵敏度 (76%) 无差,但不是特异性 (82%).
- 异常AI实现了97%的灵敏度和79%的特异性,达到其预先规定的目标.
结论:
- 在高结核病和艾滋病毒负担的人群中,CXR AI在积极的肺结核选方面表现出对放射科医生的非劣势.
- 无论是结核病AI还是放射科医生都没有满足世卫组织对研究人口的敏感性建议.
- 人工智能是一个可行的工具,用于检测CXR异常超出结核病在这个人口.
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