Advanced Deep Learning Techniques Applied to Automated Femoral Neck Fracture Detection and Classification

To use deep learning with advanced data augmentation to accurately diagnose and classify femoral neck fractures. A retrospective study of patients with femoral neck fractures was performed. One thousand sixty-three AP hip radiographs were obtained from 550 patients. Ground truth labels of Garden fracture classification were applied as follows: (1) 127 Garden I and II fracture radiographs, (2) 610 Garden III and IV fracture radiographs, and (3) 326 normal hip radiographs. After localization by an initial network, a second CNN classified the images as Garden I/II fracture, Garden III/IV fracture, or no fracture. Advanced data augmentation techniques expanded the training set: (1) generative adversarial network (GAN); (2) digitally reconstructed radiographs (DRRs) from preoperative hip CT scans. In all, 9063 images, real and generated, were available for training and testing. A deep neural network was designed and tuned based on a 20% validation group. A holdout test dataset consisted of 105 real images, 35 in each class. Two class prediction of fracture versus no fracture (AUC 0.92): accuracy 92.3%, sensitivity 0.91, specificity 0.93, PPV 0.96, NPV 0.86. Three class prediction of Garden I/II, Garden III/IV, or normal (AUC 0.96): accuracy 86.0%, sensitivity 0.79, specificity 0.90, PPV 0.80, NPV 0.90. Without any advanced augmentation, the AUC for two-class prediction was 0.80. With DRR as the only advanced augmentation, AUC was 0.91 and with GAN only AUC was 0.87. GANs and DRRs can be used to improve the accuracy of a tool to diagnose and classify femoral neck fractures.