Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI model is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 329 CXR cases with ground truth BMD measured by DXA. The model predicted BMD has a strong correlation with the gold standard DXA BMD (Pearson correlation coefficient 0.840). When applied for osteoporosis screening, it achieves a high classification performance (AUC 0.936). As the first effort in the field to use CXR scans to predict the spine BMD, the proposed algorithm holds strong potential in enabling early osteoporosis screening through routine chest X-rays and contributing to the enhancement of public health.

翻译：骨质疏松是一种常见慢性代谢性骨骼疾病,由于获得骨矿密度(BMD)检查、双能X射线吸附光度测定(DXA)的渠道有限,这种慢性代谢性骨骼疾病往往被诊断不足和治疗不足。在本文件中,我们提出了一个从胸透光(CXR)中预测BMD的方法,这是最常见、最易获得和成本低的医疗图像检查之一。我们的方法首先自动检测CXR的当地和全球骨骼结构(ROI)区域。然后开发了一个多ROI模型,利用胸透光图像中的当地和全球信息进行准确的BMD估计。我们的方法是用DXA测得的329个CXR(CXR)案件和地面真象BMD进行评价。模型预测BMD(C)与黄金标准DXA BMD(Pearson相关系数0.840)有很强的关联性。在进行骨质疏松检查时,它实现了高分级性表现(AUS 0.936)。作为实地首次使用CXR扫描利用胸部XR扫描的强力,通过XMDAs,以预测,从而预测稳定的先能,为BMDMXR的先变。提议。