Objective:To develop a deep learning model which can automatically and accurately detect osteoporotic vertebral compression fractures (OVCF) based on artificial intelligence.Methods:MRI images of 500 patients diagnosed with OVCF at The First People＇s Hospital of Guangzhou from January 2019 to October 2021 were collected retrospectively. There were 396 males and 204 females, with an age of (74.5±6.0) years. The T value of bone mineral density was -2.9±0.8. The fracture segments were L1 in 128 cases, L2 in 113 cases, L3 in 109 cases, L4 in 115 cases, and L5 in 108 cases. The multimodal layered converged network was used to train, test, and verify the robustness and generalization ability of a deep learning model based on MRI images of OVCF. The grad-cam was applied to visualize the results. The diagnostic value of the model for OVCF was assessed by comparing the diagnoses between the artificial intelligence model and 2 senior spinal surgeons on the MRI images of 30 OVCF patients randomized from the 500 ones.Results:Of the precise auxiliary diagnosis model for OVCF based on MRI images, the diagnostic accuracy was 96.7%, the sensitivity 93.5%, the specificity 88.9%, the positive predictive value 100.0%, and the negative predictive value 86.6%, all significantly higher than those of the 2 senior spinal surgeons (70.0%, 72.7%, 28.6%, 82.1%, and 28.6%) ( P<0.05). Conclusion:The present study has successfully established a deep learning model which can automatically and accurately diagnose OVCF based on MRI images, showing a high diagnostic efficiency than human spinal surgeons.