Potential of Artificial Intelligence to Generate Health Research Reports of Decayed, Missed and Restored Teeth

Authors

  • Eliana Dantas Costa Department of Dental Materials and Prosthodontics, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil Author https://orcid.org/0000-0003-4463-7436
  • José Andery Carneiro Department of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil Author https://orcid.org/0000-0003-2068-0106
  • Breno Augusto Guerra Zancan Department of Computing and Mathematics, University of São Paulo, Ribeirão Preto, São Paulo, Brazil Author https://orcid.org/0000-0003-2890-6384
  • Hugo Gaêta-Araujo Department of Stomatology, Public Health and Forensic Dentistry, Division of Oral Radiology, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil. Author https://orcid.org/0000-0001-5087-5022
  • Christiano Oliveira-Santos Department of Stomatology, Public Health and Forensic Dentistry, Division of Oral Radiology, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil. Department of Diagnosis & Oral Health, University of Louisville School of Dentistry, Louisville, KY, USA Author https://orcid.org/0000-0001-9936-7547
  • Alessandra Alaniz Macedo Department of Stomatology, Public Health and Forensic Dentistry, Division of Oral Radiology, School of Dentistry of Ribeirão Preto, University of São Paulo, Ribeirão Preto, São Paulo, Brazil. Author https://orcid.org/0000-0001-5271-3086
  • Camila Tirapelli Department of Diagnosis & Oral Health, University of Louisville School of Dentistry, Louisville, KY, USA Author https://orcid.org/0000-0001-5020-6515

DOI:

https://doi.org/10.15517/ijds.2024.59184

Keywords:

Artificial intelligence; Radiology; Dentistry; Radiography

Abstract

This study aims to indicate the potential of artificial intelligence (AI) in epidemiological reports of decayed, missed and restored teeth. As a proof of concept our study model used panoramic x-ray images and an AI algorithm for tooth numbering, detection of the caries and restorations with accuracy over 80% for such diagnostic tasks. The output came as the number of decayed, missed and restored teeth according to patient´s age and the DMFT index (number of decayed, missing, and filled teeth) which varied from 3.6 (up to 20 years old) to 20.4 (+60 years old). Thus, it is suggested that AI is a promising method to automate health data collection through the analysis of x-rays.

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Published

2026-04-28

Issue

Vol. 26 No. 2 (2024): (May-August) Odovtos-International Journal of Dental Sciences

Section

New Perspectives Article

License

Copyright (c) 2024 Eliana Dantas Costa, José Andery Carneiro, Breno Augusto Guerra Zancan, Hugo Gaêta-Araujo, Christiano Oliveira-Santos, Alessandra Alaniz Macedo, Camila Tirapelli

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