深度学习用于胸部X射线上肺胸部检测:一个诊断测试准确性系统性审查和元分析
Benjamin D Katzman1, Mostafa Alabousi2, Nabil Islam3
1Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
概括
深度学习 (DL) 算法在胸部X射线图 (CXR) 上检测肺胸部的高精度. 这种由人工智能驱动的工具可以显著加快诊断速度,并改善临床环境中的患者护理.
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:
- 肺胸炎是一种常见的急性疾病,需要通过胸部放射 (CXR) 及时诊断.
- 尽量减少诊断时间对于有效的患者治疗至关重要.
- 深度学习 (DL) 算法为快速识别成像异常提供了潜力.
研究的目的:
- 系统地审查和元分析DL算法在检测CXRs上的肺胸部的性能.
- 评估AI在诊断肺胸部的整体有效性.
主要方法:
- 根据一个注册的协议 (PROSPERO CRD42023391375) 进行了系统审查和元分析.
- 包括到2023年1月10日发表的研究,使用成年患者和计算机辅助检测CXR上的肺胸部.
- 进行了双变随机效应元分析和元回归,并使用QUADAS-2评估偏差风险.
主要成果:
- 分析了23项研究,包括34,011名患者和34,075名CXR.
- 肺胸检测的综合灵敏度为87% (95% CI,81%-92%),综合特异性为95% (95% CI,92%-97%).
- 研究设计,数据源和偏差风险并没有显著影响检测性能.
结论:
- 深度学习显示了CXRs肺胸部检测的高灵敏度和特异性.
- DL算法具有临床实施的巨大潜力,有助于放射科医生,加速患者的诊断和治疗.
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