Application of machine learning in the context of reoperation, outcome and management after ACL reconstruction - A systematic review.

Journal: The Knee
Published Date: Mar 18, 2025

Abstract

INTRODUCTION: Machine learning-based tools are becoming increasingly popular in clinical practice. They offer new possibilities but are also limited in their reliability and accuracy.

Authors

  • Julius Michael Wolfgart
    Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Hospital, 52074, Aachen, Germany.
  • Ulf Krister Hofmann
    Department of Orthopedics, Trauma and Reconstructive Surgery, Division of Arthroplasty, University Hospital Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen, Germany.
  • Maximilian Praster
    Department of Orthopaedic, Trauma, and Reconstructive Surgery, Division of Arthroplasty and Tumour Surgery, RWTH University Hospital, 52074, Aachen, Germany.
  • Marina Danalache
    Department of Orthopaedic Surgery, University Hospital Tübingen, Hoppe-Seyler Straße 3, 72076, Tübingen, Germany.
  • Filipo Migliorini
    Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Hospital, 52074 Aachen, Germany; Department of Orthopaedic and Trauma Surgery, Academic Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of the Paracelsus Medical University, 39100 Bolzano, Italy.
  • Martina Feierabend
    Metabolic Reconstruction and Flux Modelling, University of Cologne, Zülpicher Str. 47b, 50674, Cologne, Germany. mfeierab@uni-koeln.de.

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