Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review.

Journal: BMC musculoskeletal disorders
PMID: 39755642

Abstract

BACKGROUND: To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.

Authors

  • Benjamin Blackman
    School of Medicine, University of Limerick, Limerick, Ireland.
  • Prushoth Vivekanantha
    Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.
  • Rafay Mughal
    Michael DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
  • Ayoosh Pareek
    Department of Orthopaedic Surgery and Sports Medicine, Mayo Clinic, Rochester, Minnesota, USA.
  • Anthony Bozzo
    From the Division of Orthopaedic Surgery, McGill University, Canada (Bozzo), the Division of Radiation Oncology, McGill University, Canada (Tsui), the Department of Epidemiology and Biostatistics, Department of Diagnostic Radiology, McGill University, Canada (Bhatnagar), and the Memorial Sloan Kettering Cancer Center (Forsberg).
  • Kristian Samuelsson
    Department of Orthopaedics Institute of Clinical Sciences, The Sahlgrenska Academy University of Gothenburg Gothenburg Sweden.
  • Darren de Sa
    Division of Orthopaedic Surgery, Department of Surgery, McMaster University, Hamilton, ON, Canada.

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