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Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review

Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral disease...

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Autores principales: Khanagar, Sanjeev B., Alfouzan, Khalid, Awawdeh, Mohammed, Alkadi, Lubna, Albalawi, Farraj, Alfadley, Abdulmohsen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139989/
https://www.ncbi.nlm.nih.gov/pubmed/35626239
http://dx.doi.org/10.3390/diagnostics12051083
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author Khanagar, Sanjeev B.
Alfouzan, Khalid
Awawdeh, Mohammed
Alkadi, Lubna
Albalawi, Farraj
Alfadley, Abdulmohsen
author_facet Khanagar, Sanjeev B.
Alfouzan, Khalid
Awawdeh, Mohammed
Alkadi, Lubna
Albalawi, Farraj
Alfadley, Abdulmohsen
author_sort Khanagar, Sanjeev B.
collection PubMed
description Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC.
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spelling pubmed-91399892022-05-28 Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review Khanagar, Sanjeev B. Alfouzan, Khalid Awawdeh, Mohammed Alkadi, Lubna Albalawi, Farraj Alfadley, Abdulmohsen Diagnostics (Basel) Review Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC. MDPI 2022-04-26 /pmc/articles/PMC9139989/ /pubmed/35626239 http://dx.doi.org/10.3390/diagnostics12051083 Text en © 2022 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.
Alfouzan, Khalid
Awawdeh, Mohammed
Alkadi, Lubna
Albalawi, Farraj
Alfadley, Abdulmohsen
Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review
title Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review
title_full Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review
title_fullStr Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review
title_full_unstemmed Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review
title_short Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review
title_sort application and performance of artificial intelligence technology in detection, diagnosis and prediction of dental caries (dc)—a systematic review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139989/
https://www.ncbi.nlm.nih.gov/pubmed/35626239
http://dx.doi.org/10.3390/diagnostics12051083
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