Detecting Intertrochanteric Hip Fractures with Orthopedist-level Accuracy Using a Deep Convolutional Neural Network

Objective: To compare performances in diagnosing intertrochanteric hip fractures from proximal femoral radiographs between a convolutional neural network and orthopedic surgeons.

Materials And Methods: In total, 1773 patients were enrolled in this study. Hip plain radiographs from these patients were cropped to display only proximal fractured and non-fractured femurs. Images showing pseudarthrosis after femoral neck fracture and those showing artificial objects were excluded. This yielded a total of 3346 hip images (1773 fractured and 1573 non-fractured hip images) that were used to compare performances between the convolutional neural network and five orthopedic surgeons.

Results: The convolutional neural network and orthopedic surgeons had accuracies of 95.5% (95% CI = 93.1-97.6) and 92.2% (95% CI = 89.2-94.9), sensitivities of 93.9% (95% CI = 90.1-97.1) and 88.3% (95% CI = 83.3-92.8), and specificities of 97.4% (95% CI = 94.5-99.4) and 96.8% (95% CI = 95.1-98.4), respectively.

Conclusions: The performance of the convolutional neural network exceeded that of orthopedic surgeons in detecting intertrochanteric hip fractures from proximal femoral radiographs under limited conditions. The convolutional neural network has a significant potential to be a useful tool for screening for fractures on plain radiographs, especially in the emergency room, where orthopedic surgeons are not readily available.