AI Article Synopsis
- * Researchers tested an AI model on 716 patients and used 849 MRI images, achieving a diagnostic accuracy of 97.6% for fresh VFs, with very high sensitivity and specificity.
- * The results suggest that AI can significantly assist doctors in accurately diagnosing fresh VFs and making better treatment decisions, compared to traditional methods.
Article Abstract
Background: The 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.
Methods: This 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.
Results: The 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%.
Conclusions: Our findings highlighted the potential of AI in diagnosing fresh VFs, offering an accurate and efficient way to aid physicians with diagnosis and treatment decisions.
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| http://dx.doi.org/10.1016/j.wneu.2024.01.035 | DOI Listing |
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Article Synopsis
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- * The results suggest that AI can significantly assist doctors in accurately diagnosing fresh VFs and making better treatment decisions, compared to traditional methods.
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