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Machine learning in knee arthroplasty: specific data are key-a systematic review.

Florian Hinterwimmer1,2, Igor Lazic3, Christian Suren4

  • 1Department of Orthopaedics and Sports Orthopaedics, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, 81675, München, Germany. florian.hinterwimmer@tum.de.

Knee Surgery, Sports Traumatology, Arthroscopy : Official Journal of the ESSKA
|January 10, 2022
PubMed
Summary

Machine learning (ML) models can predict some outcomes in knee arthroplasty, but complex predictions remain inaccurate. Effective use requires specific data and collaboration between orthopaedic surgeons and data scientists.

Keywords:
Artificial intelligenceKnee arthroscopyKnee surgeryMachine learningSupervised learningTotal knee arthroplasty

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Area of Science:

  • Orthopaedic Surgery
  • Data Science
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) and machine learning (ML) are increasingly used in healthcare for data analysis and prediction.
  • ML applications in orthopaedics, particularly knee arthroplasty, are emerging but the literature is limited.
  • This review explores the feasibility of ML predictions in knee arthroplasty and identifies prerequisites for its effective implementation.

Purpose of the Study:

  • To systematically review machine learning (ML) algorithms for outcome prediction in knee arthroplasty.
  • To identify which predictions are currently feasible using ML models in this field.
  • To determine the prerequisites for the effective utilization of ML in knee arthroplasty.

Main Methods:

  • A systematic literature search was conducted using PubMed, Medline, and the Cochrane Library for ML applications in knee arthroplasty.
  • Eligible articles were evaluated by an orthopaedic surgeon and a data scientist based on the PRISMA statement.
  • A modified Coleman Methodology Score (mCMS) was used for methodological assessment.

Main Results:

  • Nineteen studies were included, analyzing heterogeneous prediction models for complications, costs, functional outcomes, revision, satisfaction, surgical technique, and biomechanical properties.
  • Machine learning models demonstrated fair to good predictive performance (AUC median 0.76).
  • The median mCMS score was 65, indicating moderate methodological quality.

Conclusions:

  • Machine learning models can predict specific outcomes in knee arthroplasty with current data.
  • Prediction accuracy for complex outcomes remains limited, and registry data is underutilized.
  • Collaboration between orthopaedic surgeons and data scientists, along with prospective data collection, is crucial for advancing ML in knee arthroplasty.