  Purpose: Hip fractures are a common cause of morbidity and mortality.
Automatic identification and classification of hip fractures using deep
learning may improve outcomes by reducing diagnostic errors and decreasing time
to operation. Methods: Hip and pelvic radiographs from 1118 studies were
reviewed and 3034 hips were labeled via bounding boxes and classified as
normal, displaced femoral neck fracture, nondisplaced femoral neck fracture,
intertrochanteric fracture, previous ORIF, or previous arthroplasty. A deep
learning-based object detection model was trained to automate the placement of
the bounding boxes. A Densely Connected Convolutional Neural Network (DenseNet)
was trained on a subset of the bounding box images, and its performance
evaluated on a held out test set and by comparison on a 100-image subset to two
groups of human observers: fellowship-trained radiologists and orthopaedists,
and senior residents in emergency medicine, radiology, and orthopaedics.
Results: The binary accuracy for fracture of our model was 93.8% (95% CI,
91.3-95.8%), with sensitivity of 92.7% (95% CI, 88.7-95.6%), and specificity
95.0% (95% CI, 91.5-97.3%). Multiclass classification accuracy was 90.4% (95%
CI, 87.4-92.9%). When compared to human observers, our model achieved at least
expert-level classification under all conditions. Additionally, when the model
was used as an aid, human performance improved, with aided resident performance
approximating unaided fellowship-trained expert performance. Conclusions: Our
deep learning model identified and classified hip fractures with at least
expert-level accuracy, and when used as an aid improved human performance, with
aided resident performance approximating that of unaided fellowship-trained
attendings.
