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Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries

BACKGROUND: Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make...

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Autores principales: Talpur, Sarena, Azim, Fahad, Rashid, Munaf, Syed, Sidra Abid, Talpur, Baby Alisha, Khan, Saad Jawaid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989613/
https://www.ncbi.nlm.nih.gov/pubmed/35399834
http://dx.doi.org/10.1155/2022/5032435
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author Talpur, Sarena
Azim, Fahad
Rashid, Munaf
Syed, Sidra Abid
Talpur, Baby Alisha
Khan, Saad Jawaid
author_facet Talpur, Sarena
Azim, Fahad
Rashid, Munaf
Syed, Sidra Abid
Talpur, Baby Alisha
Khan, Saad Jawaid
author_sort Talpur, Sarena
collection PubMed
description BACKGROUND: Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. OBJECTIVES: The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. MATERIALS AND METHODS: A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. RESULT: This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. CONCLUSION: In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records.
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spelling pubmed-89896132022-04-09 Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries Talpur, Sarena Azim, Fahad Rashid, Munaf Syed, Sidra Abid Talpur, Baby Alisha Khan, Saad Jawaid J Healthc Eng Review Article BACKGROUND: Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population. OBJECTIVES: The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria. MATERIALS AND METHODS: A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022. RESULT: This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%. CONCLUSION: In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records. Hindawi 2022-03-31 /pmc/articles/PMC8989613/ /pubmed/35399834 http://dx.doi.org/10.1155/2022/5032435 Text en Copyright © 2022 Sarena Talpur et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Talpur, Sarena
Azim, Fahad
Rashid, Munaf
Syed, Sidra Abid
Talpur, Baby Alisha
Khan, Saad Jawaid
Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries
title Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries
title_full Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries
title_fullStr Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries
title_full_unstemmed Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries
title_short Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries
title_sort uses of different machine learning algorithms for diagnosis of dental caries
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989613/
https://www.ncbi.nlm.nih.gov/pubmed/35399834
http://dx.doi.org/10.1155/2022/5032435
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