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.

Artificial intelligence in head and neck cancer diagnosis / 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. - In: JOURNAL OF PATHOLOGY INFORMATICS. - ISSN 2229-5089. - 13:(2022), pp. 1-6. [10.1016/j.jpi.2022.100153]

Artificial intelligence in head and neck cancer diagnosis

Eccher, Albino;Bisi, Nicola;Molteni, Gabriele
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.
2022
13
1
6
Artificial intelligence in head and neck cancer diagnosis / 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. - In: JOURNAL OF PATHOLOGY INFORMATICS. - ISSN 2229-5089. - 13:(2022), pp. 1-6. [10.1016/j.jpi.2022.100153]
Bassani, Sara; Santonicco, Nicola; Eccher, Albino; Scarpa, Aldo; Vianini, Matteo; Brunelli, Matteo; Bisi, Nicola; Nocini, Riccardo; Sacchetto, Luca; M...espandi
File in questo prodotto:
File Dimensione Formato  
main-2.pdf

Open access

Tipologia: Versione pubblicata dall'editore
Dimensione 478 kB
Formato Adobe PDF
478 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1317481
Citazioni
  • ???jsp.display-item.citation.pmc??? 8
  • Scopus 10
  • ???jsp.display-item.citation.isi??? 54
social impact