Deep learning for temporomandibular joint arthropathies: A systematic review and meta-analysis.

Journal: Journal of oral rehabilitation
Published Date: May 17, 2024

Abstract

BACKGROUND AND OBJECTIVE: The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies.

Authors

  • Rata Rokhshad
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Hossein Mohammad-Rahimi
    Division of Artificial Intelligence Imaging Research, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.
  • Fatemeh Sohrabniya
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Bahare Jafari
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Parnian Shobeiri
    School of Medicine, Tehran University of Medical Science, Tehran, Iran.
  • Ioannis A Tsolakis
    Department of Orthodontics, School of Dentistry, Aristotle University of Thessaloniki, Thessaloniki, Greece.
  • Seyed AmirHossein Ourang
    Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Ahmed S Sultan
    School of Dentistry, University of Maryland, Baltimore, MD, USA.
  • Shehryar Nasir Khawaja
    Orofacial Pain Medicine, Shaukat Khanum Memorial Cancer Hospitals and Research Centres, Lahore and Peshawar, Pakistan.
  • Roxanne Bavarian
    Department of Oral and Maxillofacial Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA.
  • Juan Martin Palomo
    Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, Cleveland, Ohio, USA.

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