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Machine learning to predict distal caries in mandibular second molars associated with impacted third molars
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 a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322059/ https://www.ncbi.nlm.nih.gov/pubmed/34326441 http://dx.doi.org/10.1038/s41598-021-95024-4 |
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author | Hur, Sung-Hwi Lee, Eun-Young Kim, Min-Kyung Kim, Somi Kang, Ji-Yeon Lim, Jae Seok |
author_facet | Hur, Sung-Hwi Lee, Eun-Young Kim, Min-Kyung Kim, Somi Kang, Ji-Yeon Lim, Jae Seok |
author_sort | Hur, Sung-Hwi |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8322059 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83220592021-07-30 Machine learning to predict distal caries in mandibular second molars associated with impacted third molars Hur, Sung-Hwi Lee, Eun-Young Kim, Min-Kyung Kim, Somi Kang, Ji-Yeon Lim, Jae Seok Sci Rep Article 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. Nature Publishing Group UK 2021-07-29 /pmc/articles/PMC8322059/ /pubmed/34326441 http://dx.doi.org/10.1038/s41598-021-95024-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Hur, Sung-Hwi Lee, Eun-Young Kim, Min-Kyung Kim, Somi Kang, Ji-Yeon Lim, Jae Seok Machine learning to predict distal caries in mandibular second molars associated with impacted third molars |
title | 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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8322059/ https://www.ncbi.nlm.nih.gov/pubmed/34326441 http://dx.doi.org/10.1038/s41598-021-95024-4 |
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