Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis.

Journal: Oral radiology
Published Date:

Abstract

PURPOSE: This study aims to review deep learning applications for detecting head and neck cancer (HNC) using magnetic resonance imaging (MRI) and radiographic data.

Authors

  • Rata Rokhshad
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Seyyede Niloufar Salehi
    Executive Secretary of Research Committee, Board Director of Scientific Society, Dental Faculty, Azad University, Tehran, Iran.
  • Amirmohammad Yavari
    Student Research Committee, School of Dentistry, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Parnian Shobeiri
    School of Medicine, Tehran University of Medical Science, Tehran, Iran.
  • Mahdieh Esmaeili
    Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
  • Nisha Manila
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group, AI On Health, Berlin, Germany.
  • Saeed Reza Motamedian
    Department of Orthodontics, School of Dentistry, & Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: drmotamedian@gmail.com.
  • Hossein Mohammad-Rahimi
    Division of Artificial Intelligence Imaging Research, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.