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Artificial intelligence in head and neck cancer diagnosis

Authors :
Sara Bassani
Nicola Santonicco
Albino Eccher
Aldo Scarpa
Matteo Vianini
Matteo Brunelli
Nicola Bisi
Riccardo Nocini
Luca Sacchetto
Enrico Munari
Liron Pantanowitz
Ilaria Girolami
Gabriele Molteni
Source :
Journal of Pathology Informatics, Vol 13, Iss , Pp 100153- (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

Introduction: Artificial intelligence (AI) is currently being used to augment histopathological diagnostics in pathology. This systematic review aims to evaluate the evolution of these AI-based diagnostic techniques for diagnosing head and neck neoplasms. Materials and methods: Articles regarding the use of AI for head and neck pathology published from 1982 until March 2022 were evaluated based on a search strategy determined by a multidisciplinary team of pathologists and otolaryngologists. Data from eligible articles were summarized according to author, year of publication, country, study population, tumor details, study results, and limitations. Results: Thirteen articles were included according to inclusion criteria. The selected studies were published between 2012 and March 1, 2022. Most of these studies concern the diagnosis of oral cancer; in particular, 6 are related to the oral cavity, 2 to the larynx, 1 to the salivary glands, and 4 to head and neck squamous cell carcinoma not otherwise specified (NOS). As for the type of diagnostics considered, 12 concerned histopathology and 1 cytology. Discussion: Starting from the pathological examination, artificial intelligence tools are an excellent solution for implementing diagnosis capability. Nevertheless, today the unavailability of large training datasets is a main issue that needs to be overcome to realize the true potential.

Details

Language :
English
ISSN :
21533539
Volume :
13
Issue :
100153-
Database :
Directory of Open Access Journals
Journal :
Journal of Pathology Informatics
Publication Type :
Academic Journal
Accession number :
edsdoj.71e049856ca44dee9be9109244712eed
Document Type :
article
Full Text :
https://doi.org/10.1016/j.jpi.2022.100153