Wu Yan, Yang Xiaopeng, Wang Mingyue, Lian Yanbang, Hou Ping, Chai Xiangfei, Dai Qiong, Qian Baoxin, Jiang Yaojun, Gao Jianbo
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of Scientific Research, Huiying Medical Technology, Beijing, China.
Eur Radiol. 2025 Apr;35(4):2287-2295. doi: 10.1007/s00330-024-11046-2. Epub 2024 Sep 4.
It is feasible to evaluate bone mineral density (BMD) and detect osteoporosis through an artificial intelligence (AI)-assisted system by using quantitative computed tomography (QCT) as a reference without additional radiation exposure or cost.
A deep-learning model developed based on 3312 low-dose chest computed tomography (LDCT) scans (trained with 2337 and tested with 975) achieved a mean dice similarity coefficient of 95.8% for T1-T12, L1, and L2 vertebral body (VB) segmentation on test data. We performed a model evaluation based on 4401 LDCT scans (obtained from scanners of 3 different manufacturers as external validation data). The BMD values of all individuals were extracted from three consecutive VBs: T12 to L2. Line regression and Bland‒Altman analyses were used to evaluate the overall detection performance. Sensitivity and specificity were used to evaluate the diagnostic performance for normal, osteopenia, and osteoporosis patients.
Compared with the QCT results as the diagnostic standard, the BMD assessed had a mean error of (- 0.28, 2.37) mg/cm. Overall, the sensitivity of a normal diagnosis was greater than that of a diagnosis of osteopenia or osteoporosis. For the diagnosis of osteoporosis, the model achieved a sensitivity > 86% and a specificity > 98%.
The developed tool is clinically applicable and helpful for the positioning and analysis of VBs, the measurement of BMD, and the screening of osteopenia and osteoporosis.
The developed system achieved high accuracy for automatic opportunistic osteoporosis screening using low-dose chest CT scans and performed well on CT images collected from different scanners.
Osteoporosis is a prevalent but underdiagnosed condition that can increase the risk of fractures. This system could automatically and opportunistically screen for osteoporosis using low-dose chest CT scans obtained for lung cancer screening. The developed system performed well on CT images collected from different scanners and did not differ with patient age or sex.
通过使用定量计算机断层扫描（QCT）作为参考的人工智能（AI）辅助系统来评估骨密度（BMD）并检测骨质疏松症是可行的，且无需额外的辐射暴露或费用。
基于3312例低剂量胸部计算机断层扫描（LDCT）扫描（2337例用于训练，975例用于测试）开发的深度学习模型在测试数据上对T1-T12、L1和L2椎体（VB）分割的平均骰子相似系数达到了95.8%。我们基于4401例LDCT扫描（从3个不同制造商的扫描仪获取作为外部验证数据）进行了模型评估。所有个体的骨密度值从三个连续椎体：T12至L2中提取。采用线性回归和布兰德-奥特曼分析来评估总体检测性能。敏感性和特异性用于评估正常、骨量减少和骨质疏松患者的诊断性能。
与作为诊断标准的QCT结果相比，评估的骨密度平均误差为（-0.28，2.37）mg/cm。总体而言，正常诊断的敏感性高于骨量减少或骨质疏松的诊断。对于骨质疏松症的诊断，该模型的敏感性>86%，特异性>98%。
所开发的工具在临床上适用，有助于椎体的定位和分析、骨密度的测量以及骨量减少和骨质疏松症的筛查。
所开发的系统在使用低剂量胸部CT扫描进行自动机会性骨质疏松症筛查方面达到了高精度，并且在从不同扫描仪收集的CT图像上表现良好。
骨质疏松症是一种普遍但诊断不足的疾病，会增加骨折风险。该系统可以使用为肺癌筛查而获取的低剂量胸部CT扫描自动且机会性地筛查骨质疏松症。所开发的系统在从不同扫描仪收集的CT图像上表现良好，且与患者年龄或性别无关。
