A Deep Convolutional Neural Network Using MRI: Automated Differentiation between Osteoporotic Vertebral Fracture and Vertebral Compression Fractures Due to Spinal Metastasis.

Retrospective study of magnetic resonance imaging (MRI).To assess the ability of a convolutional neural network (CNN) model to differentiate osteoporotic vertebral fractures (OVFs) and malignant vertebral compression fractures (MVFs) using short-TI inversion recovery (STIR) and T1-weighted images (T1WI) and to compare it to the performance of three spine surgeons.Differentiating between OVFs and MVFs is crucial for appropriate clinical staging and treatment planning. However, an accurate diagnosis is sometimes difficult. Recently, CNN modeling-an artificial intelligence technique-has gained popularity in the radiology field.We enrolled 50 patients with OVFs and 47 patients with MVFs who underwent thoracolumbar MRI. Sagittal STIR images and sagittal T1WI were used to train and validate the CNN models. To assess the performance of the CNN, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was calculated. We also compared the accuracy, sensitivity, and specificity of the diagnosis made by the CNN and three spine surgeons.The AUC of ROC curves of the CNN based on STIR images and T1WI were 0.967 and 0.984, respectively. The CNN model based on STIR images showed a performance of 93.8% accuracy, 92.5% sensitivity, and 94.9% specificity. On the other hand, the CNN model based on T1WI showed a performance of 96.4% accuracy, 98.1% sensitivity, and 94.9% specificity. The accuracy and specificity of the CNN using both STIR and T1WI were statistically equal to or better than that of three spine surgeons. There were no significant differences in sensitivity based on both STIR images and T1WI between the CNN and spine surgeons.We successfully differentiated OVFs and MVFs based on MRI with high accuracy using the CNN model, which was statistically equal or superior to that of the spine surgeons.Level of Evidence: 4.