Oral Cancer Screening by Artificial Intelligence-Oriented Interpretation of Optical Coherence Tomography Images.
Journal
Radiology research and practice
ISSN: 2090-1941
Titre abrégé: Radiol Res Pract
Pays: Egypt
ID NLM: 101566860
Informations de publication
Date de publication:
2022
2022
Historique:
received:
26
11
2021
revised:
23
03
2022
accepted:
11
04
2022
entrez:
3
5
2022
pubmed:
4
5
2022
medline:
4
5
2022
Statut:
epublish
Résumé
Early diagnosis of oral cancer is critical to improve the survival rate of patients. The current strategies for screening of patients for oral premalignant and malignant lesions unfortunately miss a significant number of involved patients. Optical coherence tomography (OCT) is an optical imaging modality that has been widely investigated in the field of oncology for identification of cancerous entities. Since the interpretation of OCT images requires professional training and OCT images contain information that cannot be inferred visually, artificial intelligence (AI) with trained algorithms has the ability to quantify visually undetectable variations, thus overcoming the barriers that have postponed the involvement of OCT in the process of screening of oral neoplastic lesions. This literature review aimed to highlight the features of precancerous and cancerous oral lesions on OCT images and specify how AI can assist in screening and diagnosis of such pathologies.
Identifiants
pubmed: 35502299
doi: 10.1155/2022/1614838
pmc: PMC9056242
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
1614838Informations de copyright
Copyright © 2022 Kousar Ramezani and Maryam Tofangchiha.
Déclaration de conflit d'intérêts
The authors declare that they have no conflicts of interest.
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