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Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review

Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence o...

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Autores principales: Khanagar, Sanjeev B., Naik, Sachin, Al Kheraif, Abdulaziz Abdullah, Vishwanathaiah, Satish, Maganur, Prabhadevi C., Alhazmi, Yaser, Mushtaq, Shazia, Sarode, Sachin C., Sarode, Gargi S., Zanza, Alessio, Testarelli, Luca, Patil, Shankargouda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8227647/
https://www.ncbi.nlm.nih.gov/pubmed/34072804
http://dx.doi.org/10.3390/diagnostics11061004
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author Khanagar, Sanjeev B.
Naik, Sachin
Al Kheraif, Abdulaziz Abdullah
Vishwanathaiah, Satish
Maganur, Prabhadevi C.
Alhazmi, Yaser
Mushtaq, Shazia
Sarode, Sachin C.
Sarode, Gargi S.
Zanza, Alessio
Testarelli, Luca
Patil, Shankargouda
author_facet Khanagar, Sanjeev B.
Naik, Sachin
Al Kheraif, Abdulaziz Abdullah
Vishwanathaiah, Satish
Maganur, Prabhadevi C.
Alhazmi, Yaser
Mushtaq, Shazia
Sarode, Sachin C.
Sarode, Gargi S.
Zanza, Alessio
Testarelli, Luca
Patil, Shankargouda
author_sort Khanagar, Sanjeev B.
collection PubMed
description Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression.
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spelling pubmed-82276472021-06-26 Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review Khanagar, Sanjeev B. Naik, Sachin Al Kheraif, Abdulaziz Abdullah Vishwanathaiah, Satish Maganur, Prabhadevi C. Alhazmi, Yaser Mushtaq, Shazia Sarode, Sachin C. Sarode, Gargi S. Zanza, Alessio Testarelli, Luca Patil, Shankargouda Diagnostics (Basel) Review Oral cancer (OC) is a deadly disease with a high mortality and complex etiology. Artificial intelligence (AI) is one of the outstanding innovations in technology used in dental science. This paper intends to report on the application and performance of AI in diagnosis and predicting the occurrence of OC. In this study, we carried out data search through an electronic search in several renowned databases, which mainly included PubMed, Google Scholar, Scopus, Embase, Cochrane, Web of Science, and the Saudi Digital Library for articles that were published between January 2000 to March 2021. We included 16 articles that met the eligibility criteria and were critically analyzed using QUADAS-2. AI can precisely analyze an enormous dataset of images (fluorescent, hyperspectral, cytology, CT images, etc.) to diagnose OC. AI can accurately predict the occurrence of OC, as compared to conventional methods, by analyzing predisposing factors like age, gender, tobacco habits, and bio-markers. The precision and accuracy of AI in diagnosis as well as predicting the occurrence are higher than the current, existing clinical strategies, as well as conventional statistics like cox regression analysis and logistic regression. MDPI 2021-05-31 /pmc/articles/PMC8227647/ /pubmed/34072804 http://dx.doi.org/10.3390/diagnostics11061004 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Khanagar, Sanjeev B.
Naik, Sachin
Al Kheraif, Abdulaziz Abdullah
Vishwanathaiah, Satish
Maganur, Prabhadevi C.
Alhazmi, Yaser
Mushtaq, Shazia
Sarode, Sachin C.
Sarode, Gargi S.
Zanza, Alessio
Testarelli, Luca
Patil, Shankargouda
Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review
title Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review
title_full Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review
title_fullStr Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review
title_full_unstemmed Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review
title_short Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review
title_sort application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: a systematic review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8227647/
https://www.ncbi.nlm.nih.gov/pubmed/34072804
http://dx.doi.org/10.3390/diagnostics11061004
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