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 cross-validation 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.
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