Study on Opportunistic CT for Osteoporosis Screening and Bone Density Prediction
Based on Deep Neural Network
PENG Tao1, ZENG Xiaohui1, LI Yang2, LI Man2, PU Bingjie1, ZHI Biao1, WANG Yongqin1
1. Department of Radiology, Affiliated Hospital of Chengdu University, Chengdu Sichuan 610081, China;
2. Department of Research, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200000, China
Abstract：Objective To establish and evaluate a deep learning neural network model for osteoporosis screening classification
and bone density prediction based on opportunistic CT. Methods The quantitative computed tomography (QCT) bone density
measurement was used as the standard, 199 cases of opportunistic CT data were selected to establish deep learning neural networks
for densely convolutional networks models for bone density binary classification and bone density value regression. Five-fold crossvalidation
and random grouping methods were used for testing, and the performance parameters of the models were calculated
and evaluated using an independent test set of 42 opportunistic CT cases from different devices. Results The receiver operating
characteristic (ROC) curves showed that the mean area under curve for the bone density binary classification model in the testing
set and the independent test set were 0.974 and 0.938, respectively. The F1 score, recall, precision, specificity, and accuracy of the
testing set were all greater than or equal to 0.91, while the aforementioned evaluation parameters for the independent test set were
all greater than 0.862. The mean absolute error of the bone density value prediction regression model in the training set, testing
set, and independent test set were 1.42, 8.52, and 13.89, respectively, and the root mean square error were 1.93, 10.80, and 20.36,
respectively. The predicted values showed a strong positive correlation with QCT bone density values. Conclusion The deep learning
neural network model based on opportunistic CT represent strong classification ability for normal and decreased bone density and
can accurately predict bone density values, avoid unnecessary radiation risks and reduce time and economic consumption, which is
conducive to effectively expanding the scope of osteoporosis screening.
彭涛1，曾小辉1，李洋2，李曼2，蒲冰洁1，植彪1，王永芹1. 基于深度神经网络的机会性CT骨质疏松筛
查和骨密度预测研究[J]. 中国医疗设备, 2024, 39(2): 57-62.
PENG Tao1, ZENG Xiaohui1, LI Yang2, LI Man2, PU Bingjie1, ZHI Biao1, WANG Yongqin1. Study on Opportunistic CT for Osteoporosis Screening and Bone Density Prediction
Based on Deep Neural Network. China Medical Devices, 2024, 39(2): 57-62.
