Distal radius fractures (DRFs) are one of the most common types of fracture and one which is often treated surgically. Standard X-rays are obtained for DRFs, and in most cases that have an intra-articular component, a routine CT is also performed. However, it is estimated that CT is only required in 20% of cases and therefore routine CT's results in the overutilisation of resources burdening radiology and emergency departments. In this study, we explore the feasibility of using deep learning to differentiate intra- and extra-articular DRFs automatically and help streamline which fractures require a CT. Retrospectively x-ray images were retrieved from 615 DRF patients who were treated with an ORIF at the Royal Brisbane and Women's Hospital. The images were classified into AO Type A, B or C fractures by three training registrars supervised by a consultant. Deep learning was utilised in a two-stage process: 1) localise and focus the region of interest around the wrist using the YOLOv5 object detection network and 2) classify the fracture using a EfficientNet-B3 network to differentiate intra- and extra-articular fractures. The distal radius region of interest (ROI) detection stage using the ensemble model of YOLO networks detected all ROIs on the test set with no false positives. The average intersection over union between the YOLO detections and the ROI ground truth was The proposed DRF classification framework using ensemble models of YOLO and EfficientNet achieved satisfactory performance in intra- and extra-articular fracture classification. This work demonstrates the potential in automatic fracture characterization using deep learning and can serve to streamline decision making for axial imaging helping to reduce unnecessary CT scans.
In laboratory tests, the ultra-high molecular weight polyethylene used for the acetabular cups of Charnley hip prostheses has a very low wear rate against steel. In the body radiographic measurements indicate that the polyethylene wears more rapidly. In order to investigate this higher wear rate, the sockets of acetabular cups removed at post-mortem have been examined using optical and electron microscopy. It has been shown that a socket wears predominantly on its superior part and that this is a direct consequence of the orientation of the cup in the body and the direction of loading of the hip. In the worn region the femoral head in effect bores out a new socket for itself, a process which is visible with the naked eye after approximately eight years. Electron microscopy shows that the predominant wear mechanism is adhesion, but after about eight years the appearance of surface cracks suggests that surface fatigue is taking place in addition to this. Laboratory wear tests have shown that pure surface fatigue is not sufficient to account for the high clinical wear rate. Other deformation processes are suggested and discussed with regard to the higher clinical wear rate.
