Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy.

Journal: Journal of endodontics
PMID: 36563779

Abstract

INTRODUCTION: The aim of this systematic review and meta-analysis was to investigate the overall accuracy of deep learning models in detecting periapical (PA) radiolucent lesions in dental radiographs, when compared to expert clinicians.

Authors

  • Soroush Sadr
    Department of Endodontics, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Hossein Mohammad-Rahimi
    Division of Artificial Intelligence Imaging Research, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.
  • 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.
  • Samira Zahedrozegar
    Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Parisa Motie
    Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Shankeeth Vinayahalingam
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
  • Omid Dianat
    Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland, School of Dentistry, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia.
  • Ali Nosrat
    Division of Endodontics, Department of Advanced Oral Sciences and Therapeutics, University of Maryland, School of Dentistry, Baltimore, Maryland; Private Practice, Centreville Endodontics, Centreville, Virginia. Electronic address: Nosrat@umaryland.edu.

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