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Artificial intelligence and machine learning in knee arthroplasty.

Hugo C Rodriguez1, Brandon D Rust2, Martin W Roche3

  • 1Larkin Community Hospital, Department of Orthopaedic Surgery, South Miami, FL, USA; Hospital for Special Surgery, West Palm Beach, FL, USA.

The Knee
|March 1, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) are transforming knee arthroplasty (KA) by enhancing preoperative planning and predicting complications. While promising, further improvements in AI predictive performance are needed for widespread clinical use in KA.

Keywords:
Artificial intelligenceDeep learningKnee arthroplastyMachine learningPatient reported outcome measuresPredictive analytics

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Area of Science:

  • Orthopedic Surgery
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly utilized in clinical medicine, with significant applications in knee arthroplasty (KA).
  • These technologies offer advanced capabilities in predictive analytics and image recognition, aiming to optimize preoperative planning, intraoperative guidance, and the anticipation of postoperative complications.
  • This systematic review focuses on the clinical impact of AI-driven tools in total and unicompartmental knee arthroplasty, emphasizing improvements in patient outcomes and operational efficiency.

Purpose of the Study:

  • To systematically review the clinical implications of artificial intelligence (AI) and machine learning (ML) applications in total and unicompartmental knee arthroplasty (KA).
  • To identify how AI/ML tools enhance patient outcomes and operational efficiency in KA procedures.
  • To assess the current state and future potential of AI/ML in improving KA care.

Main Methods:

  • A systematic literature search was performed across major databases (Cochrane, Embase, Medline, PubMed, Web of Science) up to March 2024.
  • Studies included were published in English, focused on adult human subjects, and specifically related to total or unicompartmental knee arthroplasty.
  • PRISMA guidelines were followed to ensure a comprehensive and unbiased review process.

Main Results:

  • 153 relevant studies were identified, examining ML applications across various KA aspects including imaging (28), postoperative complications (26), inpatient metrics (24), implant design (14), and revision surgery (12).
  • Key areas explored included patient-reported outcome measures (11), functional outcomes (11), and prediction models for KA (6).
  • A total of 66 ML models were described, with nearly half of the studies (48.7%) employing multiple ML approaches.

Conclusions:

  • Machine learning applications show considerable potential for improving patient outcomes in knee arthroplasty.
  • Current AI algorithms demonstrate promise but require further enhancement in predictive accuracy and performance validation.
  • Widespread clinical adoption of AI in knee arthroplasty necessitates robust evidence of improved predictive capabilities and clinical utility.