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开源深度神经网络的开发和验证,用于全面的胸部X射线读取:一项回顾性多中心研究

Yashin Dicente Cid¹, Matthew Macpherson², Louise Gervais-Andre³

¹WMG, University of Warwick, Coventry, UK.

The Lancet. Digital health

|December 9, 2023

概括

开源人工智能 (AI) 系统,X-Raydar和X-Raydar-NLP,在胸部X射线解释方面表现与放射科医生的性能相当. 这些人工智能工具可以加速临床部署,并有助于标准化报告,以改善医疗保健结果.

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:

训练有素的放射科医生短缺导致医疗保健系统的延迟.
使用人工智能 (AI) 对胸部X射线 (CXR) 的自动解释为标准化和延迟减少提供了潜力.
需要在大型数据集上训练可访问的,开源的AI系统,用于临床部署.

研究的目的:

开发和贡献开源AI系统,用于全面的胸部X射线异常检测.
创建AI工具,从图像和自由文本报告中分类常见的CXR发现.
通过自由可访问的模型加速人工智能在放射学中的临床部署.

主要方法:

开发了开源的神经网络,X-Raydar (图像分类) 和X-Raydar-NLP (用于报告分析的自然语言处理).
训练模型使用了来自6个英国国家卫生服务 (NHS) 信托 (2006-2019) 的2,513,546项CXR研究的大型回顾性数据集.
评估了三个数据集的性能:NHS数据集 (n=103,328),共识专家注释的数据集 (n=1427) 和MIMIC-CXR数据集 (n=252,374).

主要成果:

在数据集中,X-Raydar实现了高性能,平均AUC为0.919 (自动标记),0.864 (共识) 和0.842 (MIMIC-CXR).
人工智能性能与历史临床放射科医生记者相美,主要发现包括肺胸部和不透明化.
在共识集中,X-Raydar在37个发现中的27个中在平衡的准确性方面表现优于历史记者,平均改善率为13.3%.

结论:

使用全面分类的自动胸部X射线分类可以达到与人类专家相似的性能水平.
开发的开源人工智能模型展示了对外部数据的强大概括.
这些自由可用的基础模型可以显著推进人工智能研究和放射学中的临床部署.

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