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Current applications of machine learning in dentistry

Artificial intelligence (AI) is the general description given to computer systems that can perform tasks and mimic the requirement of human intelligence input (Pesapane et al., 2018). Machine learning (ML), a subset of AI was described as an algorithm with the ability to "learn" by identif...

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Main Authors:	Ghazali, Ahmad Badruddin, Reduwan, Nor Hidayah, Ibrahim, Roliana
Other Authors:	Yusoff, Yusliza
Format:	Book Chapter
Language:	English
Published:	UTM Press 2022
Subjects:	R Medicine (General) RK Dentistry RK318 Oral and Dental Medicine. Pathology. Diseases-Therapeutics-General Works RK529 Oral Surgery-General Works T Technology (General)
Online Access:	http://irep.iium.edu.my/99803/5/99803_Current%20applications%20of%20machine%20learning%20in%20dentistry.pdf http://irep.iium.edu.my/99803/
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spelling	my.iium.irep.998032022-09-06T02:50:14Z http://irep.iium.edu.my/99803/ Current applications of machine learning in dentistry Ghazali, Ahmad Badruddin Reduwan, Nor Hidayah Ibrahim, Roliana R Medicine (General) RK Dentistry RK318 Oral and Dental Medicine. Pathology. Diseases-Therapeutics-General Works RK529 Oral Surgery-General Works T Technology (General) Artificial intelligence (AI) is the general description given to computer systems that can perform tasks and mimic the requirement of human intelligence input (Pesapane et al., 2018). Machine learning (ML), a subset of AI was described as an algorithm with the ability to "learn" by identifying patterns in a large dataset (Rowe, 2019). ML programs can improve from experience automatically, unlike traditional computer programming, where every step of the program requires a written code (Mayo & Leung, 2018). The process is similar to a human expert that can learn by repeated training (Hung et al., 2019). The quality of the output depends on the quality of data used to train and validate the algorithm (Rowe, 2019). Additionally, deep learning (DL), which is a subset of ML, was inspired by the structure and function of the human brain called artificial neural network (ANN). ANN contains multiple layers of the network that receives the output of the previous layer, computing a task and sending it to another layer, and the structure is able to teach itself by reviewing a large amount of data (Mayo & Leung, 2018). Convolutional neural network (CNN) is commonly applied in computer vision research. The difference between ANN and CNN is that in CNN, only the last layer is fully connected, but in ANN, each neuron is connected with the other (Kumar, 2017). Most mathematical models were developed to find the relationship between input data and output data. However, a complex real-world phenomenon cannot be described easily from a closed-form input-output relationship. Thus, ML is an automated process to build a computational model of these complex relationships (Bastanlar & Özuysal, 2014). This chapter is organized by firstly presenting potential use of ML in dentistry in Section 1.2. Section 1.3 describes the methodology for ML research while Section 1.4 explains the applications of ML in dentistry. Section 1.5 discusses the available ML products and studies in the field of dentistry. Section 1.6 of this chapter provides the limitation and ethical consideration of ML research in dentistry, and finally Section 1.7 concludes the chapter. UTM Press Yusoff, Yusliza Sulaiman, Sarina 2022 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/99803/5/99803_Current%20applications%20of%20machine%20learning%20in%20dentistry.pdf Ghazali, Ahmad Badruddin and Reduwan, Nor Hidayah and Ibrahim, Roliana (2022) Current applications of machine learning in dentistry. In: Big Data and Machine Learning with Applications. UTM Press, pp. 161-173. ISBN 978-983-52-1857-6
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English
topic	R Medicine (General) RK Dentistry RK318 Oral and Dental Medicine. Pathology. Diseases-Therapeutics-General Works RK529 Oral Surgery-General Works T Technology (General)
spellingShingle	R Medicine (General) RK Dentistry RK318 Oral and Dental Medicine. Pathology. Diseases-Therapeutics-General Works RK529 Oral Surgery-General Works T Technology (General) Ghazali, Ahmad Badruddin Reduwan, Nor Hidayah Ibrahim, Roliana Current applications of machine learning in dentistry
description	Artificial intelligence (AI) is the general description given to computer systems that can perform tasks and mimic the requirement of human intelligence input (Pesapane et al., 2018). Machine learning (ML), a subset of AI was described as an algorithm with the ability to "learn" by identifying patterns in a large dataset (Rowe, 2019). ML programs can improve from experience automatically, unlike traditional computer programming, where every step of the program requires a written code (Mayo & Leung, 2018). The process is similar to a human expert that can learn by repeated training (Hung et al., 2019). The quality of the output depends on the quality of data used to train and validate the algorithm (Rowe, 2019). Additionally, deep learning (DL), which is a subset of ML, was inspired by the structure and function of the human brain called artificial neural network (ANN). ANN contains multiple layers of the network that receives the output of the previous layer, computing a task and sending it to another layer, and the structure is able to teach itself by reviewing a large amount of data (Mayo & Leung, 2018). Convolutional neural network (CNN) is commonly applied in computer vision research. The difference between ANN and CNN is that in CNN, only the last layer is fully connected, but in ANN, each neuron is connected with the other (Kumar, 2017). Most mathematical models were developed to find the relationship between input data and output data. However, a complex real-world phenomenon cannot be described easily from a closed-form input-output relationship. Thus, ML is an automated process to build a computational model of these complex relationships (Bastanlar & Özuysal, 2014). This chapter is organized by firstly presenting potential use of ML in dentistry in Section 1.2. Section 1.3 describes the methodology for ML research while Section 1.4 explains the applications of ML in dentistry. Section 1.5 discusses the available ML products and studies in the field of dentistry. Section 1.6 of this chapter provides the limitation and ethical consideration of ML research in dentistry, and finally Section 1.7 concludes the chapter.
author2	Yusoff, Yusliza
author_facet	Yusoff, Yusliza Ghazali, Ahmad Badruddin Reduwan, Nor Hidayah Ibrahim, Roliana
format	Book Chapter
author	Ghazali, Ahmad Badruddin Reduwan, Nor Hidayah Ibrahim, Roliana
author_sort	Ghazali, Ahmad Badruddin
title	Current applications of machine learning in dentistry
title_short	Current applications of machine learning in dentistry
title_full	Current applications of machine learning in dentistry
title_fullStr	Current applications of machine learning in dentistry
title_full_unstemmed	Current applications of machine learning in dentistry
title_sort	current applications of machine learning in dentistry
publisher	UTM Press
publishDate	2022
url	http://irep.iium.edu.my/99803/5/99803_Current%20applications%20of%20machine%20learning%20in%20dentistry.pdf http://irep.iium.edu.my/99803/
_version_	1744353524981956608
score	13.211869

Current applications of machine learning in dentistry

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