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骨质疏松症的预测来自前腰椎脊椎X射线

Ryusei Inamori¹, Tomoya Kobayashi¹, Eichi Takaya¹

¹AI Lab, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan; Department of Diagnostic Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi, 980-8575, Japan.

Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry

|January 10, 2026

概括

DeepXrayTM Spina软件通过前腰椎X射线 (FLS-X) 准确地预测骨质疏松症. 该工具在骨矿物密度 (BMD) 评估和骨质疏松症分类方面表现出高性能和可靠性.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
骨质疏松症研究骨质疏松症研究

背景情况:

骨质疏松症的诊断严重依赖于骨矿物密度 (BMD) 的测量.
双能X射线吸收度 (DXA) 是黄金标准,但可能无法获得.
前腰椎X射线 (FLS-X) 为 BMD 估计提供了一个潜在的替代方案.

研究的目的:

评估DeepXrayTM Spina软件在估计FLS-X.X的BMD和T分数方面的性能.
评估软件在预测骨质疏松症分类 (正常,骨质疏松症,骨质疏松症) 中的准确性.

主要方法:

一个日本队列在30天内接受了FLS-X和DXA.
DeepXrayTM Spina从FLS-X估计了BMD;结果与DXA测量的BMD (观察到的BMD) 相比较.
统计分析包括皮尔森相关系数 (PCC) 和正常化根平均平方误差 (NRMSE);使用准确度,灵敏度,特异性和科恩的卡帕来评估分类性能.

主要成果:

在估计和观察到的BMD之间发现了强烈的相关性 (PCC = 0.901,NRMSE = 0.070).
该软件在骨质疏松症 (0.951) 和骨质疏松症 (0.854) 的分类中取得了高准确性.
在所有分类的灵敏度和特异性方面报告了出色的性能指标,二次加权的科恩卡帕为0.884.

结论:

DeepXrayTM 脊柱在预测FLS-X骨质疏松症方面表现出高性能和稳健性.
该软件是骨质疏松症评估的实用和可靠工具,有可能改善诊断的可访问性.

关键词:

骨矿物质密度骨矿物质密度双能X射线吸收度测法双能X射线吸收度测额头腰椎脊柱X射线影像额头腰椎脊柱X射线机器学习机器学习骨质疏松症是一种骨质疏松症.

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