Application of a machine learning and optimization method to predict patellofemoral instability risk factors in children and adolescents.

Journal: Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
PMID: 39033342

Abstract

PURPOSE: Conservative treatment remains the standard approach for first-time patellar dislocations. While risk factors for patellofemoral instability, a common paediatric injury, are well-established in adults, data concerning the progression of paediatric recurrent patellar dislocation remain scarce. A reproducible method was developed to quantitatively assess the patellofemoral morphology and anatomic risk factors in paediatric patients using magnetic resonance imaging (MRI) and machine learning analysis.

Authors

  • Yoon Hae Kwak
    Department of Orthopedic Surgery, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea.
  • Yu Jin Ko
    Cell & Developmental Biology, University of Rochester, Rochester, New York, USA.
  • Hyunjae Kwon
    Department of Orthopedic Surgery, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea.
  • Yong-Gon Koh
    Department of Orthopaedic Surgery, Joint Reconstruction Center, Yonsei Sarang Hospital, Seoul, Korea.
  • Amaal M Aldosari
    Department of Orthopedic Surgery, Al Noor Specialist Hospital, Makkah, Saudi Arabia.
  • Ji-Hoon Nam
    Department of Mechanical Engineering, Yonsei University, Seoul, Korea.
  • Kyoung-Tak Kang
    Department of Mechanical Engineering, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul, 03722, Republic of Korea.

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