Artificial intelligence accurately identifies total hip arthroplasty implants: a tool for revision surgery

• Published in: Hip international 2022-11, Vol.32 (6), p.766-770
• Main Authors: Murphy, Michael, Killen, Cameron, Burnham, Robert, Sarvari, Fahad, Wu, Karen, Brown, Nicholas
• Format: Article
• Language: English
• ISSN: 1120-7000; 1724-6067
• DOI: 10.1177/1120700020987526

Background: A critical part in preoperative planning for revision arthroplasty surgery involves the identification of the failed implant. Using a predictive artificial neural network (ANN) model, the objectives of this study were: (1) to develop a machine-learning algorithm using operative big data to identify an implant from a radiograph; and (2) to compare algorithms that optimise accuracy in a timely fashion. Methods: Using 2116 postoperative anteroposterior (AP) hip radiographs of total hip arthroplasties from 2002 to 2019, 10 artificial neural networks were modeled and trained to classify the radiograph according to the femoral stem implanted. Stem brand and model was confirmed with 1594 operative reports. Model performance was determined by classification accuracy toward a random 706 AP hip radiographs, and again on a consecutive series of 324 radiographs prospectively collected over 2019. Results: The Dense-Net 201 architecture outperformed all others with 100.00% accuracy in training data, 95.15% accuracy on validation data, and 91.16% accuracy in the unique prospective series of patients. This outperformed all other models on the validation (p