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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Autor*in:	Sara Bassani [verfasserIn] Nicola Santonicco [verfasserIn] Albino Eccher [verfasserIn] Aldo Scarpa [verfasserIn] Matteo Vianini [verfasserIn] Matteo Brunelli [verfasserIn] Nicola Bisi [verfasserIn] Riccardo Nocini [verfasserIn] Luca Sacchetto [verfasserIn] Enrico Munari [verfasserIn] Liron Pantanowitz [verfasserIn] Ilaria Girolami [verfasserIn] Gabriele Molteni [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Artificial intelligence Head and neck cancer Pathology Diagnosis Whole slide imaging

Übergeordnetes Werk:	In: Journal of Pathology Informatics - Elsevier, 2012, 13(2022), Seite 100153-
Übergeordnetes Werk:	volume:13 ; year:2022 ; pages:100153-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jpi.2022.100153

Katalog-ID:	DOAJ020757409

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520			\|a 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.
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650		4	\|a Head and neck cancer
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653		0	\|a Computer applications to medicine. Medical informatics
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spelling	10.1016/j.jpi.2022.100153 doi (DE-627)DOAJ020757409 (DE-599)DOAJ71e049856ca44dee9be9109244712eed DE-627 ger DE-627 rakwb eng R858-859.7 RB1-214 Sara Bassani verfasserin aut Artificial intelligence in head and neck cancer diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Head and neck cancer Pathology Diagnosis Whole slide imaging Computer applications to medicine. Medical informatics Nicola Santonicco verfasserin aut Albino Eccher verfasserin aut Aldo Scarpa verfasserin aut Matteo Vianini verfasserin aut Matteo Brunelli verfasserin aut Nicola Bisi verfasserin aut Riccardo Nocini verfasserin aut Luca Sacchetto verfasserin aut Enrico Munari verfasserin aut Liron Pantanowitz verfasserin aut Ilaria Girolami verfasserin aut Gabriele Molteni verfasserin aut In Journal of Pathology Informatics Elsevier, 2012 13(2022), Seite 100153- (DE-627)638414660 (DE-600)2579241-6 21533539 nnns volume:13 year:2022 pages:100153- https://doi.org/10.1016/j.jpi.2022.100153 kostenfrei https://doaj.org/article/71e049856ca44dee9be9109244712eed kostenfrei http://www.sciencedirect.com/science/article/pii/S2153353922007477 kostenfrei https://doaj.org/toc/2153-3539 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 100153-
allfields_unstemmed	10.1016/j.jpi.2022.100153 doi (DE-627)DOAJ020757409 (DE-599)DOAJ71e049856ca44dee9be9109244712eed DE-627 ger DE-627 rakwb eng R858-859.7 RB1-214 Sara Bassani verfasserin aut Artificial intelligence in head and neck cancer diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Head and neck cancer Pathology Diagnosis Whole slide imaging Computer applications to medicine. Medical informatics Nicola Santonicco verfasserin aut Albino Eccher verfasserin aut Aldo Scarpa verfasserin aut Matteo Vianini verfasserin aut Matteo Brunelli verfasserin aut Nicola Bisi verfasserin aut Riccardo Nocini verfasserin aut Luca Sacchetto verfasserin aut Enrico Munari verfasserin aut Liron Pantanowitz verfasserin aut Ilaria Girolami verfasserin aut Gabriele Molteni verfasserin aut In Journal of Pathology Informatics Elsevier, 2012 13(2022), Seite 100153- (DE-627)638414660 (DE-600)2579241-6 21533539 nnns volume:13 year:2022 pages:100153- https://doi.org/10.1016/j.jpi.2022.100153 kostenfrei https://doaj.org/article/71e049856ca44dee9be9109244712eed kostenfrei http://www.sciencedirect.com/science/article/pii/S2153353922007477 kostenfrei https://doaj.org/toc/2153-3539 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 100153-
allfieldsGer	10.1016/j.jpi.2022.100153 doi (DE-627)DOAJ020757409 (DE-599)DOAJ71e049856ca44dee9be9109244712eed DE-627 ger DE-627 rakwb eng R858-859.7 RB1-214 Sara Bassani verfasserin aut Artificial intelligence in head and neck cancer diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Head and neck cancer Pathology Diagnosis Whole slide imaging Computer applications to medicine. Medical informatics Nicola Santonicco verfasserin aut Albino Eccher verfasserin aut Aldo Scarpa verfasserin aut Matteo Vianini verfasserin aut Matteo Brunelli verfasserin aut Nicola Bisi verfasserin aut Riccardo Nocini verfasserin aut Luca Sacchetto verfasserin aut Enrico Munari verfasserin aut Liron Pantanowitz verfasserin aut Ilaria Girolami verfasserin aut Gabriele Molteni verfasserin aut In Journal of Pathology Informatics Elsevier, 2012 13(2022), Seite 100153- (DE-627)638414660 (DE-600)2579241-6 21533539 nnns volume:13 year:2022 pages:100153- https://doi.org/10.1016/j.jpi.2022.100153 kostenfrei https://doaj.org/article/71e049856ca44dee9be9109244712eed kostenfrei http://www.sciencedirect.com/science/article/pii/S2153353922007477 kostenfrei https://doaj.org/toc/2153-3539 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 100153-
allfieldsSound	10.1016/j.jpi.2022.100153 doi (DE-627)DOAJ020757409 (DE-599)DOAJ71e049856ca44dee9be9109244712eed DE-627 ger DE-627 rakwb eng R858-859.7 RB1-214 Sara Bassani verfasserin aut Artificial intelligence in head and neck cancer diagnosis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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 Head and neck cancer Pathology Diagnosis Whole slide imaging Computer applications to medicine. Medical informatics Nicola Santonicco verfasserin aut Albino Eccher verfasserin aut Aldo Scarpa verfasserin aut Matteo Vianini verfasserin aut Matteo Brunelli verfasserin aut Nicola Bisi verfasserin aut Riccardo Nocini verfasserin aut Luca Sacchetto verfasserin aut Enrico Munari verfasserin aut Liron Pantanowitz verfasserin aut Ilaria Girolami verfasserin aut Gabriele Molteni verfasserin aut In Journal of Pathology Informatics Elsevier, 2012 13(2022), Seite 100153- (DE-627)638414660 (DE-600)2579241-6 21533539 nnns volume:13 year:2022 pages:100153- https://doi.org/10.1016/j.jpi.2022.100153 kostenfrei https://doaj.org/article/71e049856ca44dee9be9109244712eed kostenfrei http://www.sciencedirect.com/science/article/pii/S2153353922007477 kostenfrei https://doaj.org/toc/2153-3539 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 13 2022 100153-
language	English
source	In Journal of Pathology Informatics 13(2022), Seite 100153- volume:13 year:2022 pages:100153-
sourceStr	In Journal of Pathology Informatics 13(2022), Seite 100153- volume:13 year:2022 pages:100153-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Artificial intelligence Head and neck cancer Pathology Diagnosis Whole slide imaging Computer applications to medicine. Medical informatics
isfreeaccess_bool	true
container_title	Journal of Pathology Informatics
authorswithroles_txt_mv	Sara Bassani @@aut@@ Nicola Santonicco @@aut@@ Albino Eccher @@aut@@ Aldo Scarpa @@aut@@ Matteo Vianini @@aut@@ Matteo Brunelli @@aut@@ Nicola Bisi @@aut@@ Riccardo Nocini @@aut@@ Luca Sacchetto @@aut@@ Enrico Munari @@aut@@ Liron Pantanowitz @@aut@@ Ilaria Girolami @@aut@@ Gabriele Molteni @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	638414660
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language_de	englisch
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author	Sara Bassani
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topic_title	R858-859.7 RB1-214 Artificial intelligence in head and neck cancer diagnosis Artificial intelligence Head and neck cancer Pathology Diagnosis Whole slide imaging
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title	Artificial intelligence in head and neck cancer diagnosis
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title_full	Artificial intelligence in head and neck cancer diagnosis
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author_browse	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
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title_sort	artificial intelligence in head and neck cancer diagnosis
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title_auth	Artificial intelligence in head and neck cancer diagnosis
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Artificial intelligence in head and neck cancer diagnosis
url	https://doi.org/10.1016/j.jpi.2022.100153 https://doaj.org/article/71e049856ca44dee9be9109244712eed http://www.sciencedirect.com/science/article/pii/S2153353922007477 https://doaj.org/toc/2153-3539
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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. 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