Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach.
artificial intelligence
artificial neural network
deep learning
oral cancer
supervised learning
Journal
Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
ISSN: 1600-0714
Titre abrégé: J Oral Pathol Med
Pays: Denmark
ID NLM: 8911934
Informations de publication
Date de publication:
Feb 2023
Feb 2023
Historique:
revised:
05
12
2022
received:
28
10
2022
accepted:
15
12
2022
pubmed:
5
1
2023
medline:
25
2
2023
entrez:
4
1
2023
Statut:
ppublish
Résumé
Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence-based diagnostic approaches, with a special focus on convolutional neural networks, the state-of-the-art in artificial intelligence and deep learning. The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view. The development of artificial intelligence-based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
109-118Subventions
Organisme : The Coordination for the Improvement of Higher Education Personnel (CAPES/PROEX)
ID : 001
Organisme : The National Council for Scientific and Technological Development (CNPq)
Organisme : The Program for Institutional Internationalization (CAPES-PrInt)
ID : 88887.369713/2019-00
Organisme : The São Paulo Research Foundation (FAPESP)
ID : 2009/53839-2
Organisme : The São Paulo Research Foundation (FAPESP)
ID : 2021/14585-7
Organisme : Fundo de Apoio ao Ensino, à Pesquisa e Extensão, Universidade Estadual de Campinas (FAEPEX)
ID : 2597/21
Informations de copyright
© 2023 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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