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Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study

The aim was to predict the post-pubertal mandibular length and Y axis of growth in males by using various machine learning (ML) techniques. Cephalometric data obtained from 163 males with Class I Angle malocclusion, were used to train various ML algorithms. Analysis of variances (ANOVA) was used to...

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Autores principales: Wood, Tyler, Anigbo, Justina O., Eckert, George, Stewart, Kelton T., Dundar, Mehmet Murat, Turkkahraman, Hakan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178146/
https://www.ncbi.nlm.nih.gov/pubmed/37174945
http://dx.doi.org/10.3390/diagnostics13091553
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author Wood, Tyler
Anigbo, Justina O.
Eckert, George
Stewart, Kelton T.
Dundar, Mehmet Murat
Turkkahraman, Hakan
author_facet Wood, Tyler
Anigbo, Justina O.
Eckert, George
Stewart, Kelton T.
Dundar, Mehmet Murat
Turkkahraman, Hakan
author_sort Wood, Tyler
collection PubMed
description The aim was to predict the post-pubertal mandibular length and Y axis of growth in males by using various machine learning (ML) techniques. Cephalometric data obtained from 163 males with Class I Angle malocclusion, were used to train various ML algorithms. Analysis of variances (ANOVA) was used to compare the differences between predicted and actual measurements among methods and between time points. All the algorithms revealed an accuracy range from 95.80% to 97.64% while predicting post-pubertal mandibular length. When predicting the Y axis of growth, accuracies ranged from 96.60% to 98.34%. There was no significant interaction between methods and time points used for predicting the mandibular length (p = 0.235) and Y axis of growth (p = 0.549). All tested ML algorithms accurately predicted the post-pubertal mandibular length and Y axis of growth. The best predictors for the mandibular length were mandibular and maxillary lengths, and lower face height, while they were Y axis of growth, lower face height, and mandibular plane angle for the post-pubertal Y axis of growth. No significant difference was found among the accuracies of the techniques, except the least squares method had a significantly larger error than all others in predicting the Y axis of growth.
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spelling pubmed-101781462023-05-13 Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study Wood, Tyler Anigbo, Justina O. Eckert, George Stewart, Kelton T. Dundar, Mehmet Murat Turkkahraman, Hakan Diagnostics (Basel) Article The aim was to predict the post-pubertal mandibular length and Y axis of growth in males by using various machine learning (ML) techniques. Cephalometric data obtained from 163 males with Class I Angle malocclusion, were used to train various ML algorithms. Analysis of variances (ANOVA) was used to compare the differences between predicted and actual measurements among methods and between time points. All the algorithms revealed an accuracy range from 95.80% to 97.64% while predicting post-pubertal mandibular length. When predicting the Y axis of growth, accuracies ranged from 96.60% to 98.34%. There was no significant interaction between methods and time points used for predicting the mandibular length (p = 0.235) and Y axis of growth (p = 0.549). All tested ML algorithms accurately predicted the post-pubertal mandibular length and Y axis of growth. The best predictors for the mandibular length were mandibular and maxillary lengths, and lower face height, while they were Y axis of growth, lower face height, and mandibular plane angle for the post-pubertal Y axis of growth. No significant difference was found among the accuracies of the techniques, except the least squares method had a significantly larger error than all others in predicting the Y axis of growth. MDPI 2023-04-26 /pmc/articles/PMC10178146/ /pubmed/37174945 http://dx.doi.org/10.3390/diagnostics13091553 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wood, Tyler
Anigbo, Justina O.
Eckert, George
Stewart, Kelton T.
Dundar, Mehmet Murat
Turkkahraman, Hakan
Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study
title Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study
title_full Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study
title_fullStr Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study
title_full_unstemmed Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study
title_short Prediction of the Post-Pubertal Mandibular Length and Y Axis of Growth by Using Various Machine Learning Techniques: A Retrospective Longitudinal Study
title_sort prediction of the post-pubertal mandibular length and y axis of growth by using various machine learning techniques: a retrospective longitudinal study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10178146/
https://www.ncbi.nlm.nih.gov/pubmed/37174945
http://dx.doi.org/10.3390/diagnostics13091553
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