O098 A systematic review of the use of artificial intelligence for image analysis in total hip and total knee arthroplasty. (22nd July 2022) - Gurung, B; Liu, P; Harris, P; Tucker, K; Sochart, D; Kader, D; Field, R; Asopa, V
Abstract: Introduction: Total hip and knee arthroplasty are common orthopaedic procedures that require post-operative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI) technology has the potential to automate image analysis. This systematic review reports on how AI-based technologies are currently being used and their accuracy in image analysis following THA and TKA. Methods: EMBASE, Medline and PubMed libraries were systematically searched for articles published until 15/09/2021 using terms related to "x-ray analysis", "total hip/knee arthroplasty", and "AI". The review was performed according to the PRISMA guidelines (PROSPERO#CRD42021276876). Study quality was assessed using a modified MINORS tool. AI performance was reported using the area under the curve (AUC) and accuracy. Results: Of the 455 studies identified, 12 were included: nine reported implant identification, three described prediction of implant failure and three compared AI performance with orthopaedic surgeons. AI-based implant identification was precise (AUC 0.992–1) and most algorithms reported accuracy >90%. Two of these studies reported AI performance to be similar or superior to human experts. AI prediction of dislocation risk following THA was acceptable (AUC 76.67), diagnosis of hip implant loosening was good (accuracy 88.3%) and measurement of acetabular angles on post-operative x-rays was comparable to humans (Cohen's kappa 0.76–1.00). Conclusion: AI technology can be trained to identify implant models on post-operative x-rays with a performance that is comparable to that of human experts. However, the technology requires further development to enable analysis of other post-operative radiographic features following arthroplasty surgery that could improve patient care. Take-home message: Artificial intelligence image analysis through deep learning can classify hip and knee implants, and measure malposition and detect features of loosening of hip implants.