Detection of caries around restorations on bitewings using deep learning.

Journal: Journal of dentistry
Published Date: Feb 9, 2024

Abstract

OBJECTIVE: Secondary caries lesions adjacent to restorations, a leading cause of restoration failure, require accurate diagnostic methods to ensure an optimal treatment outcome. Traditional diagnostic strategies rely on visual inspection complemented by radiographs. Recent advancements in artificial intelligence (AI), particularly deep learning, provide potential improvements in caries detection. This study aimed to develop a convolutional neural network (CNN)-based algorithm for detecting primary caries and secondary caries around restorations using bitewings.

Authors

  • Eduardo Trota Chaves
    Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands; Graduate Program in Dentistry, School of Dentistry, Federal University of Pelotas, Pelotas, Brazil. Electronic address: eduardo.chaves@radboudumc.nl.
  • Shankeeth Vinayahalingam
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands.
  • Niels van Nistelrooij
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Postal Number 590, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
  • Tong Xi
    Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands. Tong.Xi@radboudumc.nl.
  • Vitor Henrique Digmayer Romero
    Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands; Graduate Program in Dentistry, School of Dentistry, Federal University of Pelotas, Pelotas, Brazil.
  • Tabea Flügge
    Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and HumboldtUniversität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
  • Hadi Saker
    Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, HB 6500, the Netherlands.
  • Alexander Kim
    Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, Postal Number 590, P.O. Box 9101, Nijmegen, HB 6500, the Netherlands.
  • Giana da Silveira Lima
    Graduate Program in Dentistry, School of Dentistry, Federal University of Pelotas, Pelotas, Brazil.
  • Bas Loomans
    Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands.
  • Marie-Charlotte Huysmans
    Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands.
  • Fausto Medeiros Mendes
    Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands; Department of Pediatric Dentistry, School of Dentistry, University of São Paulo, São Paulo, Brazil.
  • Maximiliano Sergio Cenci
    Department of Dentistry, Research Institute for Medical Innovation, Radboud University Medical Center, Philips van Leydenlaan 25, Nijmegen, EX 6525, the Netherlands.

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