PDF(Pubmed)
Abstract：
Screening for osteoporosis is crucial for early detection and prevention, yet it faces challenges due to the low accuracy of calcaneal quantitative ultrasound (QUS) and limited access to dual-energy X-ray absorptiometry (DXA) scans. Recent advances in AI offer a promising solution through opportunistic screening using existing medical images. This study aims to utilize deep learning techniques to develop a model that analyzes chest X-ray (CXR) images for osteoporosis screening. This study included the AI model development stage and the clinical validation stage. In the AI model development stage, the combined dataset of 5122 paired CXR images and DXA reports from the patients aged 20 to 98 years at a medical center was collected. The images were enhanced and filtered for hardware retention such as pedicle screws, bone cement, artificial intervertebral discs or severe deformity in target level of T12 and L1. The dataset was then separated into training, validating, and testing datasets for model training and performance validation. In the clinical validation stage, we collected 440 paired CXR images and DXA reports from both the TCVGH and Joy Clinic, including 304 pared data from TCVGH and 136 paired data from Joy Clinic. The pre-clinical test yielded an area under the curve (AUC) of 0.940, while the clinical validation showed an AUC of 0.946. Pearson\'s correlation coefficient was 0.88. The model demonstrated an overall accuracy, sensitivity, and specificity of 89.0%, 88.7%, and 89.4%, respectively. This study proposes an AI model for opportunistic osteoporosis screening through CXR, demonstrating good performance and suggesting its potential for broad adoption in preliminary screening among high-risk populations.
摘要：
筛查骨质疏松症对于早期发现和预防至关重要，然而,由于跟骨定量超声(QUS)的准确度较低和双能X线吸收法(DXA)扫描的使用受限,它面临着挑战.人工智能的最新进展通过使用现有医学图像的机会性筛查提供了一个有前途的解决方案。这项研究旨在利用深度学习技术来开发一个模型，分析用于骨质疏松症筛查的胸部X射线（CXR）图像。本研究包括AI模型开发阶段和临床验证阶段。在AI模型开发阶段,我们收集了医疗中心20~98岁患者的5122张配对CXR图像和DXA报告的组合数据集.对图像进行了增强和过滤，以保留诸如椎弓根螺钉之类的硬件，骨水泥，人工椎间盘或严重畸形的目标水平为T12和L1。然后将数据集分成训练，正在验证,和测试数据集，用于模型训练和性能验证。在临床验证阶段，我们从TCVGH和JoyClinic收集了440张成对的CXR图像和DXA报告，包括来自TCVGH的304个比较数据和来自JoyClinic的136个配对数据。临床前测试产生0.940的曲线下面积(AUC),而临床验证显示0.946的AUC。Pearson相关系数为0.88。该模型显示出整体准确性，灵敏度,特异性为89.0%，88.7%,89.4%,分别。这项研究提出了一种通过CXR进行机会性骨质疏松症筛查的AI模型，表现良好，并表明其在高危人群的初步筛查中具有广泛采用的潜力。
