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有助于人工智能的胸部X射线分类骨质疏松症并确定死亡风险

Dung-Jang Tsai^1,2,3, Chin Lin^2,3,4, Chin-Sheng Lin⁵

¹Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan, R.O.C.

Journal of medical systems

|January 13, 2024

概括

一个新的深度学习模型使用胸部X射线 (CXR) 准确识别骨质疏松症. 这种人工智能 (AI) 工具可以检测高死亡风险的个体,作为早期查方法.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
骨质疏松症研究骨质疏松症研究

背景情况:

骨质疏松症的诊断通常依赖于双能X射线吸收计 (DXA),这不是普遍可用的.
胸部X射线 (CXR) 是广泛可用的,可能含有骨质疏松症的迹象.
早期发现骨质疏松症对于及时干预和风险管理至关重要.

研究的目的:

开发和验证一个深度学习模型 (DLM) 来从CXR特征识别骨质疏松症.
调查人工智能检测骨质疏松症的预后影响,特别是其与所有原因死亡率的关联.
评估人工智能支持的CXR战略作为骨质疏松症早期查工具的潜力.

主要方法:

一个数据集由48,353个CXR与相应的DXAT分数组成.
一个DLM在35,633个CXR上接受了训练,以识别骨质疏松症 (CXR-OP),并在12,720个CXR上得到了验证.
使用接收器操作特征曲线 (AUC) 下的面积来评估模型性能;使用生存分析和Cox比例危险模型分析死亡风险.

主要成果:

DLM实现了高精度,AUC为0.930 (内部验证) 和0.892 (外部验证).
被认定为CXR-OP的个体表现出明显更高的全因死亡风险 (HR 2.59内部,HR 1.67内部没有DXA).
在外部验证组中观察到类似的死亡风险,证实了预后值.

结论:

使用CXR特征的DLM可以准确检测骨质疏松症.
人工智能支持的CXR分析提供了显著的预后价值,识别了增加死亡风险的个体.
这种人工智能驱动的CXR策略显示出作为早期骨质疏松症检测的可扩展和可访问的查工具的承诺.

关键词:

人工智能的人工智能是人工智能.骨质密度骨质密度骨质密度胸部X射线胸部X射线深度学习是一种深度学习.在T得分得分.

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