Impact of deep learning on pediatric elbow fracture detection: a systematic review and meta-analysis.

Journal: European journal of trauma and emergency surgery : official publication of the European Trauma Society
PMID: 39976732

Abstract

OBJECTIVES: Pediatric elbow fractures are a common injury among children. Recent advancements in artificial intelligence (AI), particularly deep learning (DL), have shown promise in diagnosing these fractures. This study systematically evaluated the performance of DL models in detecting pediatric elbow fractures.

Authors

  • Le Nguyen Binh
    College of Medicine, Taipei Medical University, Taipei, 11031, Taiwan.
  • Nguyen Thanh Nhu
    International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
  • Pham Thi Uyen Nhi
    Ho Chi Minh City Hospital of Dermato-Venereology, Ho Chi Minh City, Vietnam.
  • Do Le Hoang Son
    Department of Orthopedics and Trauma, Cho Ray Hospital, Ho Chi Minh City, Vietnam.
  • Nguyen Bach
    Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, 94117, Vietnam.
  • Hoang Quoc Huy
    Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, 94117, Vietnam.
  • Nguyen Quoc Khanh Le
    In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan. Electronic address: khanhlee@tmu.edu.tw.
  • Jiunn-Horng Kang
    Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, 252 Wuxing St, Xinyi District, 11031, Taipei City, Taiwan.

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