Automated pediatric TMJ articular disk identification and displacement classification in MRI with machine learning.

Journal: Journal of dentistry
PMID: 39952550

Abstract

OBJECTIVE: To evaluate the performance of an automated two-step model interpreting pediatric temporomandibular joint (TMJ) magnetic resonance imaging (MRI) using artificial intelligence (AI). Using deep learning techniques, the model first automatically identifies the disk and the TMJ osseous structures, and then an automated algorithm classifies disk displacement.

Authors

  • Roxana Azma
    Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada.
  • Abhilash Hareendranathan
    Department of Radiology & Diagnostic Imaging, University of Alberta, 2A2.41 WMC, 8440 - 112 St. NW, Edmonton, AB, Canada.
  • Mengxun Li
    School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Canada; Department of Prosthodontics, School of Stomatology, Wuhan University, China. Electronic address: mengxunli@whu.edu.cn.
  • Phu Nguyen
    Department of Computing Science, Faculty of Science, University of Alberta, Canada. Electronic address: pn2@ualberta.ca.
  • Assefa S Wahd
    Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, University of Alberta, Canada. Electronic address: wahd@ualberta.ca.
  • Jacob L Jaremko
    Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada.
  • Fabiana T Almeida
    Mike Petryk School of Dentistry, Faculty of Medicine and Dentistry, University of Alberta, Canada. Electronic address: fabiana@ualberta.ca.

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