As China's aging population continues to grow, the incidence of osteoporosis has been steadily increasing, posing a significant health challenge for the elderly population. Furthermore, the high cost of diagnosing and treating osteoporosis highlights the importance of early diagnosis as a key strategy to reduce both patient suffering and healthcare expenses.

The objective of this study is to develop a chest and abdominal bone mineral density (BMD) measurement model using conventional chest and abdominal CT scans, with deep neural networks and machine learning algorithms. The abdominal BMD model is subsequently employed to calibrate the chest BMD measurements, with the goal of enabling automated BMD measurement and the diagnosis of osteoporosis.

This retrospective study collected 702 patients from Suining Central Hospital in Sichuan Province who underwent both chest CT scans and quantitative CT (QCT) examinations during the period from March 2022 to June 2023 (spanning approximately one year) as research subjects. Among them, 532 patients were randomly divided into a training set (426 cases, 80%) and a validation set (106 cases, 20%). An additional 170 patients were included in the internal testing set. This study used the diagnostic results of QCT as the reference standard and employs machine learning methods such as logistic regression, stochastic gradient descent, and random forest to construct osteoporosis classification models and bone density regression models for the chest and abdomen, the established model was also tested internally. The performance of the classification models was evaluated using sensitivity, specificity, accuracy, precision, and area under the receiver operating characteristic curve (AUC), while regression model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), and R-squared.

The results showed that the AUC values for the osteoporosis classification models in the validation set were 0.948 for the chest model and 0.968 for the abdominal model. The mean absolute errors of the BMD regression models were 10.534 and 9.449, respectively. In the internal testing set, the AUC values for the classification models were 0.905 and 0.926, and the MAE for the regression models were 9.255 and 7.924, respectively. After calibration, the AUC and MAE of the chest BMD measurement model in the validation set improved to 0.967 and 10.511, respectively.

The AI-based chest and abdominal BMD measurements demonstrate a high correlation and consistency with QCT measurements, effectively diagnosing osteoporosis. The calibrated chest BMD measurement model further enhances diagnostic performance and offers significant potential for the application of chest CT scans in opportunistic osteoporosis screening.