Machine learning to predict distal caries in mandibular second molars associated with impacted third molars

Abstract Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occurrence of DCM2Ms due to the proximity wi...

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Autores principales:	Sung-Hwi Hur, Eun-Young Lee, Min-Kyung Kim, Somi Kim, Ji-Yeon Kang, Jae Seok Lim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b68fb3ad83f24b0a9ddad96649ef4dd6
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spelling	oai:doaj.org-article:b68fb3ad83f24b0a9ddad96649ef4dd62021-12-02T16:06:41ZMachine learning to predict distal caries in mandibular second molars associated with impacted third molars10.1038/s41598-021-95024-42045-2322https://doaj.org/article/b68fb3ad83f24b0a9ddad96649ef4dd62021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95024-4https://doaj.org/toc/2045-2322Abstract Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occurrence of DCM2Ms due to the proximity with M3Ms and determine the relative importance of predictive variables for DCM2Ms that are important for clinical decision making. A total of 2642 mandibular second molars adjacent to M3Ms were analyzed and DCM2Ms were identified in 322 cases (12.2%). The models were trained using logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting ML methods and were subsequently validated using testing datasets. The performance of the ML models was significantly superior to that of single predictors. The area under the receiver operating characteristic curve of the machine learning models ranged from 0.88 to 0.89. Six features (sex, age, contact point at the cementoenamel junction, angulation of M3Ms, Winter's classification, and Pell and Gregory classification) were identified as relevant predictors. These prediction models could be used to detect patients at a high risk of developing DCM2M and ultimately contribute to caries prevention and treatment decision-making for impacted M3Ms.Sung-Hwi HurEun-Young LeeMin-Kyung KimSomi KimJi-Yeon KangJae Seok LimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sung-Hwi Hur Eun-Young Lee Min-Kyung Kim Somi Kim Ji-Yeon Kang Jae Seok Lim Machine learning to predict distal caries in mandibular second molars associated with impacted third molars
description	Abstract Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occurrence of DCM2Ms due to the proximity with M3Ms and determine the relative importance of predictive variables for DCM2Ms that are important for clinical decision making. A total of 2642 mandibular second molars adjacent to M3Ms were analyzed and DCM2Ms were identified in 322 cases (12.2%). The models were trained using logistic regression, random forest, support vector machine, artificial neural network, and extreme gradient boosting ML methods and were subsequently validated using testing datasets. The performance of the ML models was significantly superior to that of single predictors. The area under the receiver operating characteristic curve of the machine learning models ranged from 0.88 to 0.89. Six features (sex, age, contact point at the cementoenamel junction, angulation of M3Ms, Winter's classification, and Pell and Gregory classification) were identified as relevant predictors. These prediction models could be used to detect patients at a high risk of developing DCM2M and ultimately contribute to caries prevention and treatment decision-making for impacted M3Ms.
format	article
author	Sung-Hwi Hur Eun-Young Lee Min-Kyung Kim Somi Kim Ji-Yeon Kang Jae Seok Lim
author_facet	Sung-Hwi Hur Eun-Young Lee Min-Kyung Kim Somi Kim Ji-Yeon Kang Jae Seok Lim
author_sort	Sung-Hwi Hur
title	Machine learning to predict distal caries in mandibular second molars associated with impacted third molars
title_short	Machine learning to predict distal caries in mandibular second molars associated with impacted third molars
title_full	Machine learning to predict distal caries in mandibular second molars associated with impacted third molars
title_fullStr	Machine learning to predict distal caries in mandibular second molars associated with impacted third molars
title_full_unstemmed	Machine learning to predict distal caries in mandibular second molars associated with impacted third molars
title_sort	machine learning to predict distal caries in mandibular second molars associated with impacted third molars
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b68fb3ad83f24b0a9ddad96649ef4dd6
work_keys_str_mv	AT sunghwihur machinelearningtopredictdistalcariesinmandibularsecondmolarsassociatedwithimpactedthirdmolars AT eunyounglee machinelearningtopredictdistalcariesinmandibularsecondmolarsassociatedwithimpactedthirdmolars AT minkyungkim machinelearningtopredictdistalcariesinmandibularsecondmolarsassociatedwithimpactedthirdmolars AT somikim machinelearningtopredictdistalcariesinmandibularsecondmolarsassociatedwithimpactedthirdmolars AT jiyeonkang machinelearningtopredictdistalcariesinmandibularsecondmolarsassociatedwithimpactedthirdmolars AT jaeseoklim machinelearningtopredictdistalcariesinmandibularsecondmolarsassociatedwithimpactedthirdmolars
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Machine learning to predict distal caries in mandibular second molars associated with impacted third molars

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