在26个国家的258,373张X射线中,完整的AI放射套件的性能:全球评估
E Cohen1, M S Ouertani1, P Beaumel2
1University Hospitals of Brussels, Brussels, Belgium.
Radiography (London, England : 1995)
|February 28, 2026
概括
这项研究验证了用于放射成像的全面AI套件,在各种全球环境中展示了强大的性能. 人工智能系统在检测异常和执行测量方面显示出高精度,支持在放射学中更广泛采用.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 计算病理学计算病理学
背景情况:
- 放射性成像对于异常的检测和表征至关重要.
- 这项研究在现实世界临床环境中评估了一套完整的放射性AI套件.
- 人工智能套件由AZmed (法国巴黎) 开发.
研究的目的:
- 进行一个大规模,国际,多中心,对放射性AI套件的回顾性评估.
- 评估AI套件在各种临床环境和患者人口统计学中的性能和通用性.
- 验证AI套件在创伤检测,胸部成像分析,自动测量和骨年龄预测方面的能力.
主要方法:
- 在2022年1月至2025年4月期间,从26个国家收集了258,373张X射线图.
- 使用AZmed Rayvolve AI套件处理所有图像.
- 通过两位读者注释建立了基本真相,第三位高级读者用于不一致;使用AUC,灵敏度,特异性和MAE分析了性能.
主要成果:
- AZtrauma算法实现了98.3%的AUC,97.4%的灵敏度和96.4%的特异性.
- AZchest显示AUC为97.8%,灵敏度为96.7%,特异性为87.9%.
- AZmeasure和AZboneage与放射科医生有很好的一致性 (MAE分别为1.8°/1.1mm和0.5年),各子组的表现一致.
结论:
- 人工智能套件在各种临床环境中表现出强大而普遍的性能.
- 验证支持AI在放射学中的更广泛的临床采用.
- 该系统有可能在全球范围内提高工作流程效率和诊断一致性.
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