A limited number of investigations with conflicting results have described perivascular lymphocytic infiltration (PVLI) in the setting of total knee arthroplasty (TKA). The purpose of this study was to determine if PVLI found in TKAs at the time of aseptic revision surgery was associated with worse clinical outcomes and survivorship. A retrospective review was conducted on 617 patients who underwent aseptic TKA revision who had histological analysis for PVLI at the time of surgery. Clinical and radiological data were obtained pre- and postoperatively, six weeks postoperatively, and then every year thereafter.Aims
Methods
Aims. The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. Methods. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset. Results. The convolutional neural network we built performed well when detecting loosening from radiographs alone. The first model built de novo with only the radiological image as input had an accuracy of 70%. The final model, which was built by fine-tuning a publicly available model named DenseNet, combining the AP and lateral radiographs, and incorporating information from the patient’s history, had an accuracy, sensitivity, and specificity of 88.3%, 70.2%, and 95.6% on the independent test dataset. It performed better for cases of
