Li Yali, Wu Yan
Department of Radiology, the First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China.
Department of Radiology, the First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China.
J Clin Densitom. 2025 Apr-Jun;28(2):101576. doi: 10.1016/j.jocd.2025.101576. Epub 2025 Feb 13.
To investigate the accuracy of an artificial intelligence (AI) prototype in determining bone mineral density (BMD) in chronic obstructive pulmonary disease (COPD) patients using chest computed tomography (CT) scans.
This study involved 1276 health checkups and 1877 COPD patients who underwent chest CT scans from April 2020 to December 2021. Automated identification, segmentation, and Hounsfield Unit (HU) measurement of the thoracic vertebrae were performed using the musculoskeletal module of the AI-Rad Companion Chest CT (Siemens Healthineers, Er langen, Germany). Patients were divided into three groups: normal BMD, osteopenia, and osteoporosis, with quantitative CT (QCT) as the standard for analysis. The correlation between the HU and BMD values from T8 to T12 and T11-T12 vertebrae was analyzed using Linear regression analysis. The diagnostic performance of the HU values from T8 to T12 and T11-T12 vertebrae for osteoporosis was evaluated using the receiver operating characteristic curve.
The HU values strongly correlated with BMD values in health checkups and COPD patients (R=0.881‒0.936 and 0.863‒0.927, P < 0.001). The Box-and-Whisker plot showed significant differences between HU and BMD values for T11-T12 vertebrae in normal BMD, osteopenia, and osteoporosis groups in two datasets (P < 0.001). The AUC was 0.970-0.982 and 0.944-0.961 in health checkups and COPD patients for detecting osteoporosis, with a sensitivity of 92.27 %‒97.42 % and 79.48 %‒90.24 % and a specificity of 86.35 %‒92.69 % and 82.81 %‒90.94 %. The optimal thresholds were 99.5‒120.5 HU and 104.5‒123.5 HU, respectively.
The AI software achieved high accuracy for automatic opportunistic osteoporosis screening in COPD patients, which may be a complementary method for quickly screening the population at high risk of osteoporosis.
研究一种人工智能（AI）原型通过胸部计算机断层扫描（CT）来测定慢性阻塞性肺疾病（COPD）患者骨密度（BMD）的准确性。
本研究纳入了2020年4月至2021年12月期间接受胸部CT扫描的1276例健康体检者和1877例COPD患者。使用AI-Rad Companion Chest CT（西门子医疗，德国埃尔朗根）的肌肉骨骼模块对胸椎进行自动识别、分割和Hounsfield单位（HU）测量。患者分为三组：骨密度正常、骨量减少和骨质疏松，以定量CT（QCT）作为分析标准。采用线性回归分析T8至T12和T11-T12椎体的HU值与BMD值之间的相关性。使用受试者工作特征曲线评估T8至T12和T11-T12椎体的HU值对骨质疏松的诊断性能。
健康体检者和COPD患者的HU值与BMD值密切相关（R=0.881‒0.936和0.863‒0.927，P<0.001）。箱线图显示，在两个数据集中，正常骨密度、骨量减少和骨质疏松组T11-T12椎体的HU值与BMD值之间存在显著差异（P<0.001）。健康体检者和COPD患者检测骨质疏松的曲线下面积（AUC）分别为0.970-0.982和0.944-0.961，灵敏度分别为92.27%‒97.42%和79.48%‒90.24%，特异性分别为86.35%‒92.69%和82.81%‒90.94%。最佳阈值分别为99.5‒120.5 HU和104.5‒123.5 HU。
该AI软件在COPD患者中进行自动机会性骨质疏松筛查具有较高的准确性，可能是快速筛查骨质疏松高危人群的一种补充方法。
