The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies.

Journal: Dento maxillo facial radiology
PMID: 39656660

Abstract

OBJECTIVES: This study aimed to assess the impact of a deep learning model on oral radiologists' ability to detect periapical radiolucencies on periapical radiographs. The secondary objective was to conduct a regression analysis to evaluate the effects of years of experience, time to diagnose, and specialty.

Authors

  • Manal H Hamdan
    Department of General Dental Sciences, Marquette University School of Dentistry, Milwaukee, WI, United States.
  • Sergio E Uribe
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Conservative Dentistry and Oral Health & Bioinformatics Research Unit, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia.
  • Lyudmila Tuzova
    Denti.AI Technology Inc, Toronto, Canada.
  • Dmitry Tuzoff
    Denti.AI Technology Inc, Toronto, Canada.
  • Zaid Badr
    Technological Innovation Center, Department of General Dental Sciences, Marquette University School of Dentistry, Milwaukee, WI 53233, USA.
  • AndrĂ© Mol
    Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States.
  • Donald A Tyndall
    Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina, Chapel Hill, NC, United States.

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