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In European journal of radiology ; h5-index 47.0

PURPOSE : Recent papers have shown the utility of deep learning in detecting hip fractures with pelvic radiographs, but there is a paucity of research utilizing deep learning to detect pelvic and acetabular fractures. Creating deep learning models also requires appropriately labeling x-ray positions and hardware presence. Our purpose is to train and test deep learning models to detect pelvic radiograph position, hardware presence, and pelvic and acetabular fractures in addition to hip fractures.

MATERIAL AND METHODS : Data was retrospectively acquired between 8/2009-6/2019. A subset of the data was split into 4 position labels and 2 hardware labels to create position labeling and hardware detecting models. The remaining data was parsed with these trained models, labeled based on 6 "separate" fracture patterns, and various fracture detecting models were created. A receiver operator characteristic (ROC) curve, area under the curve (AUC), and other output metrics were evaluated.

RESULTS : The position and hardware models performed well with AUC of 0.99-1.00. The AUC for proximal femoral fracture detection was as high as 0.95, which was in line with previously published research. Pelvic and acetabular fracture detection performance was as low as 0.70 for the posterior pelvis category and as high as 0.85 for the acetabular category with the "separate" fracture model.

CONCLUSION : We successfully created deep learning models that can detect pelvic imaging position, hardware presence, and pelvic and acetabular fractures with AUC loss of only 0.03 for proximal femoral fracture.

Kitamura Gene


Artificial intelligence, Deep learning, Fracture, Machine learning, Radiographs