Knee arthroplasty (KA), encompassing Total Knee Replacement (TKR) and Unicompartmental Knee Replacement (UKR), is one of the most common orthopedic procedures, aimed at alleviating severe knee arthritis. Postoperative KA management, especially radiographic imaging, remains a substantial financial burden and lacks standardised protocols for its clinical utility during follow-up. In this retrospective multicentre cohort study, data were analysed from January 2014 to March 2020 for adult patients undergoing primary KA at Imperial NHS Trust. Patients were followed over a five-year period. Four machine learning models were developed to evaluate if post-operative X-ray frequency can predict revision surgery. The best-performing model was used to assess the risk of revision surgery associated with different number of X-rays.Introduction
Method
Optimal management of displaced intra-articular calcaneal fractures remains controversial. The aim of this prospective cohort study was to compare the clinical and radiological outcomes of minimally invasive surgery (MIS) versus non-operative treatment in displaced intra-articular calcaneal fracture up to 2-years. All displaced intra-articular calcaneal fractures between August 2014 and January 2019 that presented to a level 1 trauma centre were considered for inclusion. The decision to treat was made by a multidisciplinary meeting. Operative treatment protocol involved sinus tarsi approach or percutaneous reduction & internal fixation. Non-operative protocol involved symptomatic management with no attempt at closed reduction. All fractures were classified, and the MOXFQ/EQ-5D-5L scores were used to assess foot and ankle and general health-related quality of life outcomes respectively.Background
Methods
Knee alignment affects both the development and surgical treatment of knee osteoarthritis. Automating femorotibial angle (FTA) and hip-knee-ankle angle (HKA) measurement from radiographs could improve reliability and save time. Further, if the gold-standard HKA from full-limb radiographs could be accurately predicted from knee-only radiographs then the need for more expensive equipment and radiation exposure could be reduced. The aim of this research is to assess if deep learning methods can predict FTA and HKA angle from posteroanterior (PA) knee radiographs. Convolutional neural networks with densely connected final layers were trained to analyse PA knee radiographs from the Osteoarthritis Initiative (OAI) database with corresponding angle measurements. The FTA dataset with 6149 radiographs and HKA dataset with 2351 radiographs were split into training, validation and test datasets in a 70:15:15 ratio. Separate models were learnt for the prediction of FTA and HKA, which were trained using mean squared error as a loss function. Heat maps were used to identify the anatomical features within each image that most contributed to the predicted angles.Abstract
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