Automatic Segmentation and Radiologic Measurement of Distal Radius Fractures Using Deep Learning.

Journal: Clinics in orthopedic surgery
PMID: 38304219

Abstract

BACKGROUND: Recently, deep learning techniques have been used in medical imaging studies. We present an algorithm that measures radiologic parameters of distal radius fractures using a deep learning technique and compares the predicted parameters with those measured by an orthopedic hand surgeon.

Authors

  • Sanglim Lee
    Department of Orthopedic Surgery, Inje University Sanggye Paik Hospital, Seoul, Korea.
  • Kwang Gi Kim
    Department of Biomedical Engineering Branch, National Cancer Center, Gyeonggi-do, South Korea.
  • Young Jae Kim
    Department of Biomedical Engineering, College of Medicine, Gachon University, Gyeonggi-do, Republic of Korea.
  • Ji Soo Jeon
    Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, South Korea.
  • Gi Pyo Lee
    Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, South Korea.
  • Kyung-Chan Kim
    Department of Orthopedic Surgery, Inje University Sanggye Paik Hospital, Seoul, Korea.
  • Suk Ha Jeon
    Department of Orthopaedic Surgery, National Medical Center, Seoul, Korea.

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