Deep learning-based automated bone age estimation for Saudi patients on hand radiograph images: a retrospective study.

Journal: BMC medical imaging
PMID: 39090563

Abstract

PURPOSE: In pediatric medicine, precise estimation of bone age is essential for skeletal maturity evaluation, growth disorder diagnosis, and therapeutic intervention planning. Conventional techniques for determining bone age depend on radiologists' subjective judgments, which may lead to non-negligible differences in the estimated bone age. This study proposes a deep learning-based model utilizing a fully connected convolutional neural network(CNN) to predict bone age from left-hand radiographs.

Authors

  • Zuhal Y Hamd
    Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
  • Amal I Alorainy
    Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
  • Mohammed A Alharbi
    Medical Imaging Department, KAAUH, Riyadh, Saudi Arabia.
  • Anas Hamdoun
    Medical Imaging Department, KAAUH, Riyadh, Saudi Arabia.
  • Arwa Alkhedeiri
    Medical Imaging Department, KAAUH, Riyadh, Saudi Arabia.
  • Shaden Alhegail
    Medical Imaging Department, KAAUH, Riyadh, Saudi Arabia.
  • Nurul Absar
    Department of Computer Science & Engineering, BGC Trust University Bangladesh, Chittagong, 4301, Bangladesh.
  • Mayeen Uddin Khandaker
    Centre for Biomedical Physics, School of Healthcare and Medical Sciences, Sunway University, Bandar Sunway 47500, Selangor, Malaysia.
  • Alexander F I Osman
    Department of Radiation Oncology, American University of Beirut Medical Center, Riad El-Solh, 1107 2020, Beirut, Lebanon.

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