Prediction of bone mineral density based on computer tomography images using deep learning model

Introduction The problem of population aging is intensifying worldwide. Osteoporosis has become an important cause affecting the health status of older populations. However, the diagnosis of osteoporosis and people's understanding of it are seriously insufficient. We aim to develop a deep learning model to automatically measure bone mineral density (BMD) and improve the diagnostic rate of osteoporosis. Methods The images of 801 subjects with 2080 vertebral bodies who underwent abdominal paired computer tomography (CT) and quantitative computer tomography (QCT) scanning was retrived from June 2020 to January 2022. The BMD of T11-L4 vertebral bodies was measured by QCT. Developing a multi-stage deep learning-based model to simulate the segmentation of the vertebral body and predict BMD. The subjects were randomly divided into training dataset, validation dataset and test dataset. Analyze the fitting effect between the BMD measured by the model and the standard BMD by QCT. Accuracy, precision, recall and f1- score were used to analyze the diagnostic performance according to categorization criterion measured by QCT. Results 410 males (51.2%) and 391 females (48.8%) were included in this study. Among them, there were 154 (19.2%) males and 118 (14.7%) females aged 23-44; 182 (22.7%) males and 205 (25.6%) females aged 45-64; 74 (9.2%) males and 68 (8.5%) females aged 65-84. The number of vertebral bodies in the training dataset, the validation dataset, and the test dataset was 1433, 243, 404, respectively. In each dataset, the BMD of males and females decreases with age. There was a significant correlation between the BMD measured by the model and QCT, with the coefficient of determination (r2) 0.95-0.97. The diagnostic accuracy based on the model in the three datasets was 0.88, 0.91, and 0.91, respectively. Conclusion The proposed multi-stage deep learning-based model can achieve automatic measurement of vertebral BMD and performed well in the prediction of osteoporosis.