人工智能与手动解读在检测肺高血压时的比较准确性跨胸部成像模式:一个诊断测试准确性的元分析
Faizan Ahmed1, Faseeh Haider2, Ramsha Ali3
1Department of Medicine, Jersey Shore University Medical Center, Hackensack Meridian Health, Neptune, NJ, United States.
Frontiers in artificial intelligence
|January 29, 2026
概括
人工智能 (AI) 显著提高了使用医学成像的肺高血压 (PH) 诊断准确度. 人工智能集成显示出比手动解读更高的灵敏度和特异性,增强了跨各种模式的PH检测.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 心脏病学和肺病学诊断 诊断 心脏病学和肺病学
背景情况:
- 肺高血压 (PH) 影响全球每百万成年人中有49-55人.
- 准确及时诊断PH对于患者的治疗结果至关重要.
- 对于PH的传统诊断方法的准确性和效率可能受到限制.
研究的目的:
- 系统地评估人工智能 (AI) 与手动解释用于肺高血压 (PH) 检测的比较诊断准确性.
- 综合各种胸部成像模式的证据,包括X射线,心声学,CT和心脏MRI.
- 通过元分析,对AI在PH诊断中的表现进行定量评估.
主要方法:
- 根据PRISMA指南,在五个主要数据库中进行了全面的文献搜索.
- 使用统计软件 (R) 进行了对12项涉及7459名患者的研究的元分析.
- 使用双变随机效应模型,将灵敏度,特异性和诊断几率比率 (DOR) 结合起来;使用逻辑转换数据对AUC值进行了元分析.
主要成果:
- 人工智能整合表明,PH的诊断准确性在统计学上显著改善,AUC的AUC为0.934.
- 人工智能模型实现了0.83的聚合灵敏度和0.91的特异性,超过了手动解释.
- 虽然AUC的异质性很低,但对灵敏度和DOR的异质性很大,这表明AI模型性能存在变化.
结论:
- 人工智能集成成像显著提高了多种成像方式的肺高血压的诊断准确度.
- 与手动解释相比,人工智能表现出更高的灵敏度和特异性,在PH诊断方面提供了有前途的进步.
- 建议进行进一步的高质量试验,并进行外部验证,以证实研究结果,并解决AI模型在不同临床环境中的性能变异性.
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