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人工智能算法的临床验证,用于将肺结核和肺部发现分类在胸部X射线图中

Thiago Fellipe Ortiz de Camargo^1,2, Guilherme Alberto Sousa Ribeiro^1,2, Maria Carolina Bueno da Silva¹

¹Image Research Center, Hospital Israelita Albert Einstein, São Paulo, Brazil.

Frontiers in artificial intelligence

|February 24, 2025

概括

这项研究表明,用于胸部X射线 (CXR) 解释的AI算法与具有不同经验的医生相比性能良好. 人工智能工具对于非专业人士来说具有实用性,对医生决策的影响最小.

科学领域:

医疗成像中的人工智能
放射学和诊断成像放射学和诊断成像
为医疗保健提供深度学习.

背景情况:

胸部X射线 (CXR) 解释对于诊断肺部疾病至关重要.
非专家医生在准确的CXR解释方面经常面临挑战.
人工智能 (AI) 提供了改进CXR分析的潜力,特别是当在真实世界的数据上进行训练时.

研究的目的:

评估用于CXR解释的深度学习算法.
评估人工智能算法对非专业医生的实用性.
将AI算法的性能与胸部放射科医生的黄金标准进行比较.

主要方法:

一项回顾性研究 (2021年1月 - 2023年7月) 评估了三种模型的人工智能算法 (肺部异常,放射性发现,结核病).
使用准确度,灵敏度和特异性来测量性能.
两组医生 (经验不同 - 组A;董事会认证的胸部放射科医生 - 组B) 验证了模型,评估了同意和决策影响.

主要成果:

内部验证显示AUC高 (0.94为肺异常/肺结核,0.84为放射性发现).
外部验证显示,人工智能算法在11个类别中的6个类别的灵敏度超过了A组的医生.
A组的医生比B组 (21.75%),对AI热图的同意程度更高 (37.56%);93%的医生表示对决策没有影响.

结论:

人工智能算法在CXR解释方面表现出有效性,其性能与不同经验水平的医生相美.
人工智能工具在支持非专业医生进行放射性解释方面表现有前途.
医生报告说,人工智能对他们的最终诊断决定的影响很小.

关键词:

人工智能的人工智能是人工智能.胸部X射线胸部X射线临床验证临床验证卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.

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