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深度学习模型从胸部X射线扫描进行骨质疏松症查:对外部强度和模型校准的多中心分析

Yuichi Imoto^1,2, Takahiro Inui^1,2, Kentaro Matsui^1,2

¹Department of Orthopaedic Surgery, Teikyo University School of Medicine, Tokyo, JPN.

|September 8, 2025

概括

这项研究开发了深度学习模型,使用胸部放射 (CR) 来选骨质疏松症. 这些模型对广泛的骨质疏松症查有希望,通过外部数据集成来提高准确性.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

骨质疏松症是一种普遍的疾病,通常是晚期诊断的,导致骨折.
目前的黄金标准诊断 (DXA) 无法进行广泛的查.
胸部放射 (CR) 是常见的,提供了一个潜在的查方式.

研究的目的:

开发和评估使用CR进行骨质疏松症查的深度学习模型.
在内部和外部数据集上评估模型性能.
为了改善模型的可通用性,用于不同的临床环境.

主要方法:

开发了深度学习模型,从CR中估计骨矿物质密度 (BMD).
在内部数据集上进行训练和验证的模型.
在来自不同设施和设备的外部数据集上测试模型性能.
研究了将外部数据纳入培训以加强校准的影响.

主要成果:

模型在内部数据上预测骨质疏松症方面表现出高准确性.
外部数据的性能超过了形定量超声波 (QUS) 查.
外部数据的表现最初很差,但在将至少500个外部样本纳入培训后显著改善.
该模型使用CR成功选了骨质疏松症,没有额外的成本或侵入性.

结论:

深度学习模型可以有效地从随时可用的胸部X射线图中选骨质疏松症.
用外部数据对模型进行校准对于在不同设置中可靠的性能至关重要.
这种方法为全球骨质疏松症查提供了一种具有成本效益和可访问性的方法.

关键词:

人工智能的人工智能是人工智能.校准校准的时间胸部X射线图片胸部X射线图片胸部X射线 (CX射线) 是指胸部的X射线.深度学习是一种深度学习.外部稳固性外部稳固性通过外部验证.骨质疏松症查骨质疏松症查

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