Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas.

Journal: Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
PMID: 37712132

Abstract

BACKGROUND: Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra-and inter observer variability and improve diagnostic objectivity and reproducibility.

Authors

  • Daniela Giraldo-Roldán
    Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.
  • Erin Crespo Cordeiro Ribeiro
    Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, Brazil.
  • Anna Luíza Damaceno Araújo
    Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.
  • Paulo Victor Mendes Penafort
    Department of Oral Diagnosis, Piracicaba Dental School, State University of Campinas, Piracicaba, Brazil.
  • Viviane Mariano da Silva
    Institute of Science and Technology, Federal University of São Paulo, São José dos Campos, Brazil.
  • Jeconias Câmara
    Department of Pathology and Legal Medicine, School of Medicine, Federal University of Amazon, Manaus, Brazil.
  • Hélder Antônio Rebelo Pontes
    Oral Diagnosis Department (Pathology and Semiology), Piracicaba Dental School, University of Campinas, Piracicaba, Brazil; Oral Pathology Department, João de Barros Barreto University Hospital, Federal University of Pará, Belém, Brazil. Electronic address: Harp@ufpa.br.
  • Manoela Domingues Martins
    Department of Oral Pathology, School of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
  • Márcio Campos Oliveira
    Department of Health, State University of Feira de Santana (UEFS), Feira de Santana, Brazil.
  • Alan Roger Santos-Silva
    Oral Diagnosis Department (Pathology and Semiology), Piracicaba Dental School, University of Campinas, Piracicaba, Brazil.
  • Márcio Ajudarte Lopes
    Oral Diagnosis Department (Pathology and Semiology), Piracicaba Dental School, University of Campinas, Piracicaba, Brazil.
  • Luiz Paulo Kowalski
    Department of Head and Neck Surgery and Otorhinolaryngology, AC Camargo Cancer Center, Sao Paulo, Brazil.
  • Matheus Cardoso Moraes
    Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil.
  • Pablo Agustin Vargas
    Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (FOP-UNICAMP), Piracicaba, São Paulo, Brazil.

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