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基于深度学习的骨质疏松症诊断模型与常规胸部X射线在临床队列中的性能评估

Bona Koo¹, Yelin Roh¹, Gyubin Shin¹

¹School of Medicine, Ewha Womans University, Seoul, South Korea.

Journal of thoracic disease

|December 11, 2025

概括

一个新的深度学习模型,PROS® CXR:OSTEO,从胸部X射线预测骨质疏松症,提供可访问的查. 这种人工智能工具显示出高准确度,有助于在不同人群中早期检测.

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:

双能X射线吸收计 (DXA) 是骨质疏松症诊断的标准,但其可访问性有限.
传统的胸部X射线图广泛使用,可以用于骨质疏松症查.

研究的目的:

开发和评估一个深度学习模型 (PROS® CXR:OSTEO) 用于使用胸部X射线图来预测骨质疏松症.
评估模型的性能与DXA衍生T分数对比.

主要方法:

一项对80名成年患者进行的回顾性研究,这些患者接受了DXA和胸部X射线.
使用了基于卷积神经网络和转移学习的深度学习模型.
模型的性能与大腿骨和腰椎T-score进行了评估.

主要成果:

该模型实现了股骨的曲线下的面积 (AUC) 为0.94和腰椎的0.93.
骨质疏松症查显示90%的灵敏度和81%的特异性.
在女性患者中观察到更高的预测性表现,在男性中观察到更多的错误阳性.

结论:

PROS® CXR:OSTEO通过胸部X射线实现了机会性,低成本的骨质疏松症查.
这种人工智能驱动的方法显示出在老龄化和资源有限的人群中早期检测的潜力.
需要进一步优化以提高特异性并减少临床使用的错误阳性.

关键词:

胸部X射线胸部X射线人工智能 (AI) 是一种人工智能.深度学习是一种深度学习.早期诊断的早期诊断是什么骨质疏松症是一种骨质疏松症.

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