Arthroplasty (Mar 2022)

Artificial intelligence in diagnosis of knee osteoarthritis and prediction of arthroplasty outcomes: a review

  • Lok Sze Lee,
  • Ping Keung Chan,
  • Chunyi Wen,
  • Wing Chiu Fung,
  • Amy Cheung,
  • Vincent Wai Kwan Chan,
  • Man Hong Cheung,
  • Henry Fu,
  • Chun Hoi Yan,
  • Kwong Yuen Chiu

DOI
https://doi.org/10.1186/s42836-022-00118-7
Journal volume & issue
Vol. 4, no. 1
pp. 1 – 9

Abstract

Read online

Abstract Background Artificial intelligence is an emerging technology with rapid growth and increasing applications in orthopaedics. This study aimed to summarize the existing evidence and recent developments of artificial intelligence in diagnosing knee osteoarthritis and predicting outcomes of total knee arthroplasty. Methods PubMed and EMBASE databases were searched for articles published in peer-reviewed journals between January 1, 2010 and May 31, 2021. The terms included: ‘artificial intelligence’, ‘machine learning’, ‘knee’, ‘osteoarthritis’, and ‘arthroplasty’. We selected studies focusing on the use of AI in diagnosis of knee osteoarthritis, prediction of the need for total knee arthroplasty, and prediction of outcomes of total knee arthroplasty. Non-English language articles and articles with no English translation were excluded. A reviewer screened the articles for the relevance to the research questions and strength of evidence. Results Machine learning models demonstrated promising results for automatic grading of knee radiographs and predicting the need for total knee arthroplasty. The artificial intelligence algorithms could predict postoperative outcomes regarding patient-reported outcome measures, patient satisfaction and short-term complications. Important weaknesses of current artificial intelligence algorithms included the lack of external validation, the limitations of inherent biases in clinical data, the requirement of large datasets in training, and significant research gaps in the literature. Conclusions Artificial intelligence offers a promising solution to improve detection and management of knee osteoarthritis. Further research to overcome the weaknesses of machine learning models may enhance reliability and allow for future use in routine healthcare settings.

Keywords