Huang Chengbin, Wu Dengying, Wang Bingzhang, Hong Chenxuan, Hu Jiasen, Yan Zijian, Chen Jianpeng, Jin Yaping, Zhang Yingze
Department of Orthopaedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China.
Department of Orthopaedics, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, Zhejiang Province, China.
Insights Imaging. 2025 Jan 10;16(1):10. doi: 10.1186/s13244-024-01817-2.
A large number of middle-aged and elderly patients have an insufficient understanding of osteoporosis and its harm. This study aimed to establish and validate a convolutional neural network (CNN) model based on unenhanced chest computed tomography (CT) images of the vertebral body and skeletal muscle for opportunistic screening in patients with osteoporosis.
Our team retrospectively collected clinical information from participants who underwent unenhanced chest CT and dual-energy X-ray absorptiometry (DXA) examinations between January 1, 2022, and December 31, 2022, at four hospitals. These participants were divided into a training set (n = 581), an external test set 1 (n = 229), an external test set 2 (n = 198) and an external test set 3 (n = 118). Five CNN models were constructed based on chest CT images to screen patients with osteoporosis and compared with the SMI model to predict the performance of osteoporosis patients.
All CNN models have good performance in predicting osteoporosis patients. The average F1 score of Densenet121 in the three external test sets was 0.865. The area under the curve (AUC) of Desenet121 in external test set 1, external test set 2, and external test set 3 were 0.827, 0.859, and 0.865, respectively. Furthermore, the Densenet121 model demonstrated a notably superior performance compared to the SMI model in predicting osteoporosis patients.
The CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures.
The CNN model based on unenhanced chest CT vertebral and skeletal muscle images can opportunistically screen patients with osteoporosis. Clinicians can use the CNN model to intervene in patients with osteoporosis and promptly avoid fragility fractures.
The application of unenhanced chest CT is increasing. Most people do not consciously use DXA to screen themselves for osteoporosis. A deep learning model was constructed based on CT images from four institutions.
大量中老年患者对骨质疏松症及其危害认识不足。本研究旨在建立并验证一种基于椎体和骨骼肌的胸部非增强计算机断层扫描（CT）图像的卷积神经网络（CNN）模型，用于骨质疏松症患者的机会性筛查。
我们的团队回顾性收集了2022年1月1日至2022年12月31日期间在四家医院接受胸部非增强CT和双能X线吸收法（DXA）检查的参与者的临床信息。这些参与者被分为训练集（n = 581）、外部测试集1（n = 229）、外部测试集2（n = 198）和外部测试集3（n = 118）。基于胸部CT图像构建了五个CNN模型，用于筛查骨质疏松症患者，并与SMI模型进行比较，以预测骨质疏松症患者的表现。
所有CNN模型在预测骨质疏松症患者方面均表现良好。Densenet121在三个外部测试集中的平均F1分数为0.865。Desenet121在外部测试集1、外部测试集2和外部测试集3中的曲线下面积（AUC）分别为0.827、0.859和0.865。此外，在预测骨质疏松症患者方面，Densenet121模型表现出明显优于SMI模型的性能。
基于胸部非增强CT椎体和骨骼肌图像的CNN模型可对骨质疏松症患者进行机会性筛查。临床医生可使用该CNN模型对骨质疏松症患者进行干预，及时避免脆性骨折。
基于胸部非增强CT椎体和骨骼肌图像的CNN模型可对骨质疏松症患者进行机会性筛查。临床医生可使用该CNN模型对骨质疏松症患者进行干预，及时避免脆性骨折。
胸部非增强CT的应用正在增加。大多数人不会自觉使用DXA进行骨质疏松症筛查。基于四个机构提供的CT图像构建了一个深度学习模型。
