Deep learning detection of subtle fractures using staged algorithms to mimic radiologist search pattern

Objective: To develop and evaluate a two-stage deep convolutional neural network system that mimics a radiologist’s search pattern for detecting two small fractures: triquetral avulsion fractures and Segond fractures. Materials and methods: We obtained 231 lateral wrist radiographs and 173 anteroposterior knee radiographs from the Stanford MURA and LERA datasets and the public domain to train and validate a two-stage deep convolutional neural network system: (1) object detectors that crop the dorsal triquetrum or lateral tibial condyle, trained on control images, followed by (2) classifiers for triquetral and Segond fractures, trained on a 1:1 case:control split. A second set of classifiers was trained on uncropped images for comparison. External test sets of 50 lateral wrist radiographs and 24 anteroposterior knee radiographs were used to evaluate generalizability. Gradient-class activation mapping was used to inspect image regions of greater importance in deciding the final classification. Results: The object detectors accurately cropped the regions of interest in all validation and test images. The two-stage system achieved cross-validated area under the receiver operating characteristic curve values of 0.959 and 0.989 on triquetral and Segond fractures, compared with 0.860 (p = 0.0086) and 0.909 (p = 0.0074), respectively, for a one-stage classifier. Two-stage cross-validation accuracies were 90.8% and 92.5% for triquetral and Segond fractures, respectively. Conclusion: A two-stage pipeline increases accuracy in the detection of subtle fractures on radiographs compared with a one-stage classifier and generalized well to external test data. Focusing attention on specific image regions appears to improve detection of subtle findings that may otherwise be missed.