Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images

Abstract

Background context: Accurate diagnosis of osteoporotic vertebral fracture (OVF) is important for improving treatment outcomes; however, the gold standard has not been established yet. A deep-learning approach based on convolutional neural network (CNN) has attracted attention in the medical imaging field.

Purpose: To construct a CNN to detect fresh OVF on magnetic resonance (MR) images.

Study design/setting: Retrospective analysis of MR images PATIENT SAMPLE: This retrospective study included 814 patients with fresh OVF. For CNN training and validation, 1624 slices of T1-weighted MR image were obtained and used.

Outcome measure: We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC) in order to evaluate the performance of the CNN. Consequently, the sensitivity, specificity, and accuracy of the diagnosis by CNN and that of the two spine surgeons were compared.

Methods: We constructed an optimal model using ensemble method by combining nine types of CNNs to detect fresh OVFs. Furthermore, two spine surgeons independently evaluated 100 vertebrae, which were randomly extracted from test data.

Results: The ensemble method using VGG16, VGG19, DenseNet201, and ResNet50 was the combination with the highest AUC of ROC curves. The AUC was 0.949. The evaluation metrics of the diagnosis (CNN/surgeon 1/surgeon 2) for 100 vertebrae were as follows: sensitivity: 88.1%/88.1%/100%; specificity: 87.9%/86.2%/65.5%; accuracy: 88.0%/87.0%/80.0%.

Conclusions: In detecting fresh OVF using MR images, the performance of the CNN was comparable to that of two spine surgeons.

Keywords: Artificial intelligence; Convolutional neural network; Deep learning; Ensemble method; Fresh fracture; Magnetic resonance image; Old fracture; Osteoporosis; Osteoporotic vertebral fracture; Vertebra detection.