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Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization

Background: Midpalatal suture maturation and ossification status is the basis for appraising maxillary transverse developmental status. Methods: We established a midpalatal suture cone-beam computed tomography (CBCT) normalized database of the growth population, including 1006 CBCT files from 690 pa...

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Detalles Bibliográficos
Autores principales: Gao, Lu, Chen, Zhiyu, Zang, Lin, Sun, Zhipeng, Wang, Qing, Yu, Guoxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9311955/
https://www.ncbi.nlm.nih.gov/pubmed/35877367
http://dx.doi.org/10.3390/bioengineering9070316
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author Gao, Lu
Chen, Zhiyu
Zang, Lin
Sun, Zhipeng
Wang, Qing
Yu, Guoxia
author_facet Gao, Lu
Chen, Zhiyu
Zang, Lin
Sun, Zhipeng
Wang, Qing
Yu, Guoxia
author_sort Gao, Lu
collection PubMed
description Background: Midpalatal suture maturation and ossification status is the basis for appraising maxillary transverse developmental status. Methods: We established a midpalatal suture cone-beam computed tomography (CBCT) normalized database of the growth population, including 1006 CBCT files from 690 participants younger than 24 years old. The midpalatal suture region of interest (ROI) labeling was completed by two experienced clinical experts. The CBCT image fusion algorithm and image texture feature analysis algorithm were constructed and optimized. The age range prediction convolutional neural network (CNN) was conducted and tested. Results: The midpalatal suture fusion images contain complete semantic information for appraising midpalatal suture maturation and ossification status during the fast growth and development period. Correlation and homogeneity are the two texture features with the strongest relevance to chronological age. The overall performance of the age range prediction CNN model is satisfactory, especially in the 4 to 10 years range and the 17 to 23 years range, while for the 13 to 14 years range, the model performance is compromised. Conclusions: The image fusion algorithm can help show the overall perspective of the midpalatal suture in one fused image effectively. Furthermore, clinical decisions for maxillary transverse deficiency should be appraised by midpalatal suture image features directly rather than by age, especially in the 13 to 14 years range.
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spelling pubmed-93119552022-07-26 Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization Gao, Lu Chen, Zhiyu Zang, Lin Sun, Zhipeng Wang, Qing Yu, Guoxia Bioengineering (Basel) Article Background: Midpalatal suture maturation and ossification status is the basis for appraising maxillary transverse developmental status. Methods: We established a midpalatal suture cone-beam computed tomography (CBCT) normalized database of the growth population, including 1006 CBCT files from 690 participants younger than 24 years old. The midpalatal suture region of interest (ROI) labeling was completed by two experienced clinical experts. The CBCT image fusion algorithm and image texture feature analysis algorithm were constructed and optimized. The age range prediction convolutional neural network (CNN) was conducted and tested. Results: The midpalatal suture fusion images contain complete semantic information for appraising midpalatal suture maturation and ossification status during the fast growth and development period. Correlation and homogeneity are the two texture features with the strongest relevance to chronological age. The overall performance of the age range prediction CNN model is satisfactory, especially in the 4 to 10 years range and the 17 to 23 years range, while for the 13 to 14 years range, the model performance is compromised. Conclusions: The image fusion algorithm can help show the overall perspective of the midpalatal suture in one fused image effectively. Furthermore, clinical decisions for maxillary transverse deficiency should be appraised by midpalatal suture image features directly rather than by age, especially in the 13 to 14 years range. MDPI 2022-07-14 /pmc/articles/PMC9311955/ /pubmed/35877367 http://dx.doi.org/10.3390/bioengineering9070316 Text en © 2022 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
Gao, Lu
Chen, Zhiyu
Zang, Lin
Sun, Zhipeng
Wang, Qing
Yu, Guoxia
Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization
title Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization
title_full Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization
title_fullStr Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization
title_full_unstemmed Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization
title_short Midpalatal Suture CBCT Image Quantitive Characteristics Analysis Based on Machine Learning Algorithm Construction and Optimization
title_sort midpalatal suture cbct image quantitive characteristics analysis based on machine learning algorithm construction and optimization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9311955/
https://www.ncbi.nlm.nih.gov/pubmed/35877367
http://dx.doi.org/10.3390/bioengineering9070316
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