Accurate Automatic Detection Method to Assist Physicians in Diagnosing Early Nondisplaced Fractures of Femoral Neck

Background - Nondisplaced femoral neck fractures are sometimes misdiagnosed by radiographs. We developed an automatic detection method using deep learning networks to pinpoint femoral neck fractures on radiographs to assist the physicians in making accurate diagnosis in the first place. Results - A total of approximately 3,840 images of non-displaced Garden type I and II femoral neck fracture cases collected from the Radiology Information System (RIS) from the Picture Archiving and Communication System (PACS) database between 2018 and 2020 from the China Medical University Hospital (CMUH). Two senior orthopedic surgeons from the China Medical University Hospital participated in independently labeling the femoral neck margin and fracture line on these images as the training dataset for the deep learning network. Our proposed accurate automatic detection method, called direction-aware fracture detection network (DAFDNet), consists of two steps, namely region of interest (ROI) segmentation and fracture detection. The first step removes the noise region and pinpoints the femoral neck region. The fracture detection step uses direction-aware deep learning algorithm to mark the exact femoral neck fracture location in the region detected in the first step.Conclusions - Our proposed DAFDNet demonstrated over 94.8% accuracy in differentiating non-displaced Garden type I and type II femoral neck fracture cases. Our DAFDNet method outperforms the diagnostic accuracy of general practitioners and orthopedic surgeons in accurately locating Garden type I and type II fractures locations. This study can determine the feasibility of applying artificial intelligence in a clinical setting and how the use of deep learning networks assist physicians in improving the correct diagnosis compared to current traditional orthopedic manual assessments.