• Yan-Ni Wang, Gang Liu, Lei Wang, Chao Chen, Zhi Wang, Shan Zhu, Wen-Tao Wan, Yuan-Zhi Weng, Weijia William Lu, Zhao-Yang Li, Zheng Wang, Xin-Long Ma, and Qiang Yang.
• Department of Spine Surgery, Tianjin Hospital, Tianjin University, Tianjin, China.
• World Neurosurg. 2024 Mar 1; 183: e818e824e818-e824.

BackgroundThe accurate diagnosis of fresh vertebral fractures (VFs) was critical to optimizing treatment outcomes. Existing studies, however, demonstrated insufficient accuracy, sensitivity, and specificity in detecting fresh fractures using magnetic resonance imaging (MRI), and fall short in localizing the fracture sites.MethodsThis prospective study comprised 716 patients with fresh VFs. We obtained 849 Short TI Inversion Recovery (STIR) image slices for training and validation of the AI model. The AI models employed were yolov7 and resnet50, to detect fresh VFs.ResultsThe AI model demonstrated a diagnostic accuracy of 97.6% for fresh VFs, with a sensitivity of 98% and a specificity of 97%. The performance of the model displayed a high degree of consistency when compared to the evaluations by spine surgeons. In the external testing dataset, the model exhibited a classification accuracy of 92.4%, a sensitivity of 93%, and a specificity of 92%.ConclusionsOur findings highlighted the potential of AI in diagnosing fresh VFs, offering an accurate and efficient way to aid physicians with diagnosis and treatment decisions.Copyright © 2024 Elsevier Inc. All rights reserved.

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