人工智能用于慢性阻塞性肺疾病患者霍斯菲尔德单元的机会性骨质疏松症筛查

IF 1.7 4区医学 Q4 ENDOCRINOLOGY & METABOLISM

Journal of Clinical Densitometry Pub Date : 2025-02-13 DOI:10.1016/j.jocd.2025.101576

Yali Li, Yan Wu

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引用次数: 0

摘要

前言：研究人工智能（AI）原型在使用胸部计算机断层扫描（CT）确定慢性阻塞性肺疾病（COPD）患者骨矿物质密度（BMD）的准确性。方法：本研究涉及1276名健康检查和1877名COPD患者，他们在2020年4月至2021年12月期间接受了胸部CT扫描。使用AI-Rad胸部CT的肌肉骨骼模块（Siemens Healthineers, Er langen, Germany）对胸椎进行自动识别、分割和霍斯菲尔德单元（Hounsfield Unit， HU）测量。将患者分为骨密度正常组、骨质减少组和骨质疏松组，以定量CT （QCT）作为分析标准。采用线性回归分析T8 ~ T12和t11 ~ T12椎体HU与BMD值的相关性。采用受试者工作特征曲线评价T8 ~ T12和t11 ~ T12椎体HU值对骨质疏松症的诊断价值。结果：健康体检及COPD患者的HU值与BMD值呈强相关（R2= 0.881-0.936、0.863-0.927，P值 <； 0.001）。盒须图显示，在两个数据集中，骨密度正常组、骨质疏松组和骨质疏松组中，T11-T12椎体的HU值和骨密度值存在显著差异（P <； 0.001）。健康体检及COPD患者骨质疏松检测AUC分别为0.970 ~ 0.982、0.944 ~ 0.961，敏感性分别为92.27 % ~ 97.42 %、79.48 % ~ 90.24 %，特异性分别为86.35 % ~ 92.69 %、82.81 % ~ 90.94 %。最佳阈值分别为99.5 ~ 120.5 HU和104.5 ~ 123.5 HU。结论：AI软件在COPD患者骨质疏松自动机会性筛查中取得了较高的准确率，可作为快速筛查骨质疏松高危人群的一种补充方法。

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Artificial intelligence for opportunistic osteoporosis screening with a Hounsfield Unit in chronic obstructive pulmonary disease patients

Introduction: 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.

Methodology: 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.

Results: The HU values strongly correlated with BMD values in health checkups and COPD patients (R²=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.

Conclusions: 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.

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来源期刊

Journal of Clinical Densitometry 医学-内分泌学与代谢

CiteScore

4.90

自引率

8.00%

发文量

审稿时长

90 days

期刊介绍： The Journal is committed to serving ISCD''s mission - the education of heterogenous physician specialties and technologists who are involved in the clinical assessment of skeletal health. The focus of JCD is bone mass measurement, including epidemiology of bone mass, how drugs and diseases alter bone mass, new techniques and quality assurance in bone mass imaging technologies, and bone mass health/economics. Combining high quality research and review articles with sound, practice-oriented advice, JCD meets the diverse diagnostic and management needs of radiologists, endocrinologists, nephrologists, rheumatologists, gynecologists, family physicians, internists, and technologists whose patients require diagnostic clinical densitometry for therapeutic management.