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: fellowshiptrained 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.

Osseointegration is a surgical approach that permitted the direct attachment of an external prosthesis to the skeleton in some select patients with amputation, who had failed to tolerate conventional sockets, thereby obviating related issues such as discomfort, skin breakdown, and poor fit. In this specific population, osseointegration offers the potential for enhanced biomechanical advantage and rehabilitative potential. Multiple percutaneous implant systems exist for clinical use internationally, each attempting to create a stable bone-implant interface while avoiding complications such as infection and loosening. Prospective clinical trials are now underway in the United States. This article will review the history and biology of osseointegration, indications and contraindications for use of currently available implant systems, and reported outcomes. Future directions of orthopaedic osseointegration technology, including electronic systems capable of biomimetic bidirectional volitional motor control of, and sensory/proprioceptive feedback from, external prosthetic devices, will also be discussed.