Automatic opportunistic osteoporosis screening using chest X-ray images via deep neural networks.

Background: Osteoporosis is a bone disease characterized by reduced bone mineral density and quality, which increases the risk of fragility fractures. The current diagnostic gold standard, dual-energy X-ray absorptiometry (DXA), faces limitations such as low equipment penetration, high testing costs, and radiation exposure, restricting its feasibility as a screening tool.

Method: To address these limitations, We retrospectively collected data from 1995 patients who visited Daping Hospital in Chongqing from January 2019 to August 2024. We developed an opportunistic screening method using chest X-rays. Furthermore, we designed three innovative deep neural network models using transfer learning: Inception v3, VGG16, and ResNet50. These models were evaluated based on their classification performance for osteoporosis using chest X-ray images, with external validation via multi-center data.

Results: The ResNet50 model demonstrated superior performance, achieving average accuracies of 87.85 % and 90.38 % in the internal test dataset across two experiments, with AUC values of 0.945 and 0.957, respectively. These results outperformed traditional convolutional neural networks. In the external validation, the ResNet50 model achieved an AUC of 0.904, accuracy of 89 %, sensitivity of 90 %, and specificity of 88.57 %, demonstrating strong generalization ability. And the model shows robust performance despite concurrent pulmonary pathologies.

Conclusions: This study provides an automatic screening method for osteoporosis using chest X-rays, without additional radiation exposure or cost. The ResNet50 model's high performance supports clinicians in the early identification and treatment of osteoporosis patients.