Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial Intelligence: A Systematic Review.

Journal: Caries research
PMID: 38342096

Abstract

INTRODUCTION: A growing number of studies on diagnostic imaging show superior efficiency and accuracy of computer-aided diagnostic systems compared to those of certified dentists. This methodological systematic review aimed to evaluate the different methodological approaches used by studies focusing on machine learning and deep learning that have used radiographic databases to classify, detect, and segment dental caries.

Authors

  • Amadou Diaw Ndiaye
    Service d'Odontologie Conservatrice-Endodontie, Université Cheikh Anta Diop, Dakar, Senegal, adnndiaye@gmail.com.
  • Marie Agnès Gasqui
    Laboratoire des Multimatériaux et Interfaces (LMI), UMR CNRS, Université Claude Bernard Lyon 1, Lyon, France.
  • Fabien Millioz
  • Matthieu Perard
    University Rennes, INSERM, Rennes, France.
  • Fatou Leye Benoist
    Service d'Odontologie Conservatrice-Endodontie, Université Cheikh Anta Diop, Dakar, Senegal.
  • Brigitte Grosgogeat
    Université de Lyon, Lyon, France. brigitte.grosgogeat@univ-lyon1.fr.

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