Automated identification of arthroplasty implants could aid in pre-operative planning and is a task which could be facilitated through artificial intelligence (AI) and deep learning. The purpose of this study was to develop and test the performance of a deep learning system (DLS) for automated identification and classification of knee arthroplasty (KA) on radiographs. We collected 237 AP knee radiographs with equal proportions of native knees, total KA (TKA), and unicompartmental KA (UKA), as well as 274 radiographs with equal proportions of Smith & Nephew Journey and Zimmer NexGen TKAs. Data augmentation was used to increase the number of images available for DLS development. These images were used to train, validate, and test deep convolutional neural networks (DCNN) to 1) detect the presence of TKA; 2) differentiate between TKA and UKA; and 3) differentiate between the 2 TKA models. Receiver operating characteristic (ROC) curves were generated with area under the curve (AUC) calculated to assess test performance.Introduction
Methods
It was hypothesised that preserving a layer of gliding tissue, the parietal layer of the ulnar bursa, between the contents of the carpal tunnel and the soft tissues incised during carpal tunnel surgery might reduce scar pain and improve grip strength and function following open carpal tunnel decompression. Patients consented to randomisation to treatment with either preservation of the parietal layer of the ulnar bursa beneath the flexor retinaculum at the time of open carpal tunnel decompression (57 patients) or division of this gliding layer as part of a standard open carpal tunnel decompression (61 patients). Grip strength was measured, scar pain was rated and the validated Patient Evaluation Measure questionnaire was used to assess symptoms and disability pre-operatively and at eight to nine weeks following surgery in seventy-seven women and thirty-four men; the remaining seven patients were lost to follow-up.Background
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