Machine learning-based prediction of the necessity for the surgical treatment of distal radius fractures.

Journal: Journal of orthopaedic surgery and research
PMID: 40287717

Abstract

BACKGROUND: Treatments for distal radius fractures (DRFs) are determined by various factors. Therefore, quantitative or qualitative tools have been introduced to assist in deciding the treatment approach. This study aimed to develop a machine learning (ML) model that determines the need for surgical treatment in patients with DRFs using a ML model that incorporates various clinical data concatenated with plain radiographs in the anteroposterior and lateral views.

Authors

  • Jongmin Lim
    Department of Computer Science and Engineering, Sungkyunkwan University College of Computing and Informatics, Suwon, South Korea.
  • Sehun Chang
    Department of Computer Science and Engineering, Sungkyunkwan University College of Computing and Informatics, Suwon, South Korea.
  • Kwangsu Kim
    Department of Computer Science and Engineering, Sungkyunkwan University College of Computing and Informatics, Suwon, South Korea.
  • Hee Jin Park
    Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea.
  • Eugene Kim
    Department of Biological Science, University of Calgary, Calgary, AB, Canada.
  • Seok Woo Hong
    Department of Orthopedic Surgery, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea. poisoxic@naver.com.

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