Tsai Hung-Lung, Cheng Kuan-Hung, Lin Po-Cheng, Hsu Ying-Lin, Chen Shih-Wei
Department of Information, Tungs' Taichung MetroHarbor Hospital, Taichung, Taiwan.
Doctoral Program in Big Data Analytics for Industrial Applications, College of Science, National Chung Hsing University, Taichung, Taiwan.
PLoS One. 2025 Sep 5;20(9):e0330080. doi: 10.1371/journal.pone.0330080. eCollection 2025.
With an increasing aging population, the prevalence of chronic comorbidities is on the rise. The potential relationship between obstructive sleep apnea (OSA) and osteoporosis has garnered significant attention. Most studies examining the association between these two conditions have relied on dual-energy X-ray absorptiometry (DXA) to evaluate bone mineral density (BMD). Although DXA is considered the gold standard for BMD assessment, it does not reflect overall skeletal health. The limitations of conventional measurements are particularly pronounced in patients with multisystemic diseases, such as OSA. To address these limitations, we applied VeriOsteo™OP software to predict lumbar spine BMD and T-scores from chest X-ray (CXR) images. In addition, we examined the relationship between these bone health metrics and OSA. A total of 70,395 patients who underwent CXR examinations at Tungs' Taichung MetroHarbor Hospital from 2017 to 2022 were included. Eligible samples were selected based on the presence of an OSA ICD-10-CM diagnosis code along with DXA results. By incorporating variables, such as gender, age, Body Mass Index (BMI), and T-score, we used multiple machine learning models, including logistic regression, random forest, and XGBoost, to analyze the risk for OSA. The results indicated that when the BMI range was controlled, the predictive contribution of the T-score became significant. For some models, the area under the curve (AUC) reached over 85% in both the training and validation datasets. This suggests a notable association between T-score and OSA, which is maintained when confounding variables such as BMI are controlled. This study highlights the potential of artificial intelligence (AI) technology using CXR imaging for osteoporosis screening. Combining CXR with machine learning models enables the assessment of OSA risk and offers a cost-effective, radiation-free screening tool with promising clinical applications.
随着人口老龄化加剧，慢性合并症的患病率不断上升。阻塞性睡眠呼吸暂停（OSA）与骨质疏松症之间的潜在关系已引起广泛关注。大多数研究这两种疾病之间关联的研究都依赖双能X线吸收法（DXA）来评估骨密度（BMD）。尽管DXA被认为是BMD评估的金标准，但它并不能反映整体骨骼健康状况。传统测量方法的局限性在患有多系统疾病的患者中尤为明显，比如OSA患者。为解决这些局限性，我们应用VeriOsteo™OP软件从胸部X线（CXR）图像预测腰椎BMD和T值。此外，我们还研究了这些骨骼健康指标与OSA之间的关系。纳入了2017年至2022年在台中医院接受CXR检查的70395名患者。根据存在OSA的ICD-10-CM诊断代码以及DXA结果选择符合条件的样本。通过纳入性别、年龄、体重指数（BMI）和T值等变量，我们使用了多种机器学习模型，包括逻辑回归、随机森林和XGBoost，来分析OSA的风险。结果表明，当BMI范围得到控制时，T值的预测作用变得显著。对于一些模型，训练集和验证集的曲线下面积（AUC）均超过85%。这表明T值与OSA之间存在显著关联，在控制BMI等混杂变量时这种关联依然存在。本研究突出了利用CXR成像的人工智能（AI）技术在骨质疏松症筛查方面的潜力。将CXR与机器学习模型相结合能够评估OSA风险，并提供一种具有成本效益、无辐射的筛查工具，具有广阔的临床应用前景。
