Predictive utility of the machine learning algorithms in predicting tendinopathy: a meta-analysis of diagnostic test studies.

Journal: European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
PMID: 39985636

Abstract

BACKGROUND: Tendinopathy, a degenerative condition of tendon collagen protein, is a common sports injury among elite athletes. Despite its prevalence, the manifestation and progression of tendinopathy remain unclear, and the efficiency of diagnosis and treatment modalities is uncertain. Artificial intelligence and machine learning (ML) have shown positive results in disease diagnosis and treatment evaluation. This systematic review examined many ML methods and their diagnostic yield in predicting tendinopathy.

Authors

  • Duncan Muir
    Royal Berkshire Hospitals NHS Trust, Reading, UK. duncan.muir@nhs.net.
  • Ahmed Elgebaly
    Smart Health Centre, University of East London, London, UK.
  • Woo Jae Kim
    Surrey and Sussex Healthcare NHS Trust, Redhill, UK.
  • Ahmad Althaher
    Smart Health Centre, University of East London, London, UK.
  • Ali Narvani
    Ashford and St Peter's Hospitals NHS Foundation Trust , Chertsey, UK.
  • Mohamed A Imam
    Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK; Smart Health Centre, University of East London, University Way, London, E16 2RD, United Kingdom. Electronic address: m.imam1@nhs.net.

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