Automatic bone age assessment: a Turkish population study.

Journal: Diagnostic and interventional radiology (Ankara, Turkey)
Published Date: Mar 17, 2025

Abstract

PURPOSE: Established methods for bone age assessment (BAA), such as the Greulich and Pyle atlas, suffer from variability due to population differences and observer discrepancies. Although automated BAA offers speed and consistency, limited research exists on its performance across different populations using deep learning. This study examines deep learning algorithms on the Turkish population to enhance bone age models by understanding demographic influences.

Authors

  • Samet Öztürk
    Esenler Obstetrics & Gynecology and Pediatrics Hospital, Radiology Clinic, Istanbul, Turkiye. Electronic address: drozturksamet@gmail.com.
  • Murat Yüce
    Icahn School of Medicine at Mount Sinai Biomedical Engineering and Imaging Institute, New York, USA.
  • Gül Gizem Pamuk
    Bagcilar Training and Research Hospital, Radiology Clinic, Istanbul, Turkiye.
  • Candan Varlık
    Bagcilar Training and Research Hospital, Radiology Clinic, Istanbul, Turkiye.
  • Ahmet Tan Cimilli
    Bagcilar Training and Research Hospital, Radiology Clinic, Istanbul, Turkiye.
  • Musa Atay
    University of Health Sciences Türkiye, Bağcılar Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye.

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