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.
Application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: a systematic review / Khanagar, S. B.; Naik, S.; Al Kheraif, A. A.; Vishwanathaiah, S.; Maganur, P. C.; Alhazmi, Y.; Mushtaq, S.; Sarode, S. C.; Sarode, G. S.; Zanza, A.; Testarelli, L.; Patil, S.. - In: DIAGNOSTICS. - ISSN 2075-4418. - 11:6(2021). [10.3390/diagnostics11061004]
Application and performance of artificial intelligence technology in oral cancer diagnosis and prediction of prognosis: a systematic review
Zanza A.Writing – Original Draft Preparation
;Testarelli L.Penultimo
Conceptualization
;
2021
Abstract
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.File | Dimensione | Formato | |
---|---|---|---|
Khanagar _Application and Performance_2021.pdf
accesso aperto
Note: https://www.mdpi.com/2075-4418/11/6/1004
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Creative commons
Dimensione
718.45 kB
Formato
Adobe PDF
|
718.45 kB | Adobe PDF |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.