Understanding the use of artificial intelligence for implant analysis in total joint arthroplasty: a systematic review

• Published in: Arthroplasty Vol. 5; no. 1; p. 54
• Main Authors: Shah, Aakash K., Lavu, Monish S., Hecht, Christian J., Burkhart, Robert J., Kamath, Atul F.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 03-11-2023; BMC
• Subjects: Artificial intelligence; Implant failure; Implant recognition; Measurements; Review; Total joint arthroplasty
• ISSN: 2524-7948; 2524-7948
• DOI: 10.1186/s42836-023-00209-z

Abstract
Introduction
In recent years, there has been a significant increase in the development of artificial intelligence (AI) algorithms aimed at reviewing radiographs after total joint arthroplasty (TJA). This disruptive technology is particularly promising in the context of preoperative planning for revision TJA. Yet, the efficacy of AI algorithms regarding TJA implant analysis has not been examined comprehensively.
Methods
PubMed, EBSCO, and Google Scholar electronic databases were utilized to identify all studies evaluating AI algorithms related to TJA implant analysis between 1 January 2000, and 27 February 2023 (PROSPERO study protocol registration: CRD42023403497). The mean methodological index for non-randomized studies score was 20.4 ± 0.6. We reported the accuracy, sensitivity, specificity, positive predictive value, and area under the curve (AUC) for the performance of each outcome measure.
Results
Our initial search yielded 374 articles, and a total of 20 studies with three main use cases were included. Sixteen studies analyzed implant identification, two addressed implant failure, and two addressed implant measurements. Each use case had a median AUC and accuracy above 0.90 and 90%, respectively, indicative of a well-performing AI algorithm. Most studies failed to include explainability methods and conduct external validity testing.
Conclusion
These findings highlight the promising role of AI in recognizing implants in TJA. Preliminary studies have shown strong performance in implant identification, implant failure, and accurately measuring implant dimensions. Future research should follow a standardized guideline to develop and train models and place a strong emphasis on transparency and clarity in reporting results.
Level of Evidence
Level III.