Machine learning is better than surgeons at assessing unicompartmental knee replacement radiographs.

Journal: The Knee
Published Date: Nov 30, 2024

Abstract

BACKGROUND: Poor results occasionally occur after unicompartmental knee replacement (UKR). It is often difficult, even for experienced surgeons, to determine why patients have poor outcomes from radiographs. The aim was to compare the ability of experienced surgeons and machine learning to predict whether patients had poor or excellent outcomes from radiographs.

Authors

  • S Jack Tu
    Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford OX3 7LD, United Kingdom. Electronic address: jack.tu@ndorms.ox.ac.uk.
  • Sara Kendrick
    Indiana University School of Medicine, 340 W. 10th Street Fairbanks Hall., Indianapolis, IN 46202, United States.
  • Karthik Saravanan
    Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford OX3 7LD, United Kingdom.
  • Christopher Dodd
    Oxford University Hospitals NHS Foundation Trust, Nuffield Orthopaedic Centre, Old Road, Oxford OX3 7HE, United Kingdom.
  • David W Murray
    Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford OX3 7LD, United Kingdom; Oxford University Hospitals NHS Foundation Trust, Nuffield Orthopaedic Centre, Old Road, Oxford OX3 7HE, United Kingdom.
  • Stephen J Mellon
    Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Windmill Road, Oxford OX3 7LD, United Kingdom. Electronic address: stephen.mellon@ndorms.ox.ac.uk.

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