Analysis of the mandibular canal course using unsupervised machine learning algorithm

<h4>Objectives</h4> Anatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for cla...

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Autores principales:	Young Hyun Kim, Kug Jin Jeon, Chena Lee, Yoon Joo Choi, Hoi-In Jung, Sang-Sun Han
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7694a3287ec848e898ae7d88f2270768
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spelling	oai:doaj.org-article:7694a3287ec848e898ae7d88f22707682021-11-25T06:19:28ZAnalysis of the mandibular canal course using unsupervised machine learning algorithm1932-6203https://doaj.org/article/7694a3287ec848e898ae7d88f22707682021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8604350/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Objectives</h4> Anatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population. <h4>Materials and methods</h4> A total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis. <h4>Results</h4> Cluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference. <h4>Conclusion</h4> The MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.Young Hyun KimKug Jin JeonChena LeeYoon Joo ChoiHoi-In JungSang-Sun HanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Young Hyun Kim Kug Jin Jeon Chena Lee Yoon Joo Choi Hoi-In Jung Sang-Sun Han Analysis of the mandibular canal course using unsupervised machine learning algorithm
description	<h4>Objectives</h4> Anatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population. <h4>Materials and methods</h4> A total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis. <h4>Results</h4> Cluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference. <h4>Conclusion</h4> The MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.
format	article
author	Young Hyun Kim Kug Jin Jeon Chena Lee Yoon Joo Choi Hoi-In Jung Sang-Sun Han
author_facet	Young Hyun Kim Kug Jin Jeon Chena Lee Yoon Joo Choi Hoi-In Jung Sang-Sun Han
author_sort	Young Hyun Kim
title	Analysis of the mandibular canal course using unsupervised machine learning algorithm
title_short	Analysis of the mandibular canal course using unsupervised machine learning algorithm
title_full	Analysis of the mandibular canal course using unsupervised machine learning algorithm
title_fullStr	Analysis of the mandibular canal course using unsupervised machine learning algorithm
title_full_unstemmed	Analysis of the mandibular canal course using unsupervised machine learning algorithm
title_sort	analysis of the mandibular canal course using unsupervised machine learning algorithm
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7694a3287ec848e898ae7d88f2270768
work_keys_str_mv	AT younghyunkim analysisofthemandibularcanalcourseusingunsupervisedmachinelearningalgorithm AT kugjinjeon analysisofthemandibularcanalcourseusingunsupervisedmachinelearningalgorithm AT chenalee analysisofthemandibularcanalcourseusingunsupervisedmachinelearningalgorithm AT yoonjoochoi analysisofthemandibularcanalcourseusingunsupervisedmachinelearningalgorithm AT hoiinjung analysisofthemandibularcanalcourseusingunsupervisedmachinelearningalgorithm AT sangsunhan analysisofthemandibularcanalcourseusingunsupervisedmachinelearningalgorithm
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Analysis of the mandibular canal course using unsupervised machine learning algorithm

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