Deep learning for age estimation from panoramic radiographs: A systematic review and meta-analysis.

Journal: Journal of dentistry
PMID: 39826609

Abstract

INTRODUCTION: Panoramic radiographs are widely used for age estimation in clinical and forensic domains. Conventionally, age estimation uses humans assessing tooth development and deducing the expected age from that. Deep learning may improve or substitute this traditional approach and allow age estimation at scale in routine settings. The objective of this systematic review was to assess the performance of deep learning for age estimation on panoramic radiographs.

Authors

  • Rata Rokhshad
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Fateme Nasiri
    Isfahan University of Medical Sciences, Faculty of Dentistry, Isfahan, Iran.
  • Naghme Saberi
    Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
  • Reyhane Shoorgashti
    Oral Medicine Department, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
  • Sarah Sadat Ehsani
    Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
  • Zahra Nasiri
    Department of Physics, University of Alabama at Birmingham, Birmingham, USA.
  • Ali Azadi
    GRIAL Research Group, Computer Science Department, Universidad de Salamanca, Salamanca, Spain.
  • Falk Schwendicke
    Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, Berlin, Germany. falk.schwendicke@charite.de.

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