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Artificial intelligence in head and neck cancer diagnosis

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 th...

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Detalles Bibliográficos
Autores principales: Bassani, Sara, Santonicco, Nicola, Eccher, Albino, Scarpa, Aldo, Vianini, Matteo, Brunelli, Matteo, Bisi, Nicola, Nocini, Riccardo, Sacchetto, Luca, Munari, Enrico, Pantanowitz, Liron, Girolami, Ilaria, Molteni, Gabriele
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9808017/
https://www.ncbi.nlm.nih.gov/pubmed/36605112
http://dx.doi.org/10.1016/j.jpi.2022.100153
Descripción
Sumario: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.