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Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study

Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to a...

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Autores principales: Rauf, Aras Maruf, Mahmood, Trefa Mohammed Ali, Mohammed, Miran Hikmat, Omer, Zana Qadir, Kareem, Fadil Abdullah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10673436/
https://www.ncbi.nlm.nih.gov/pubmed/38004022
http://dx.doi.org/10.3390/medicina59111973
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author Rauf, Aras Maruf
Mahmood, Trefa Mohammed Ali
Mohammed, Miran Hikmat
Omer, Zana Qadir
Kareem, Fadil Abdullah
author_facet Rauf, Aras Maruf
Mahmood, Trefa Mohammed Ali
Mohammed, Miran Hikmat
Omer, Zana Qadir
Kareem, Fadil Abdullah
author_sort Rauf, Aras Maruf
collection PubMed
description Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to avoid future crowding in growing patients or even in adult patients seeking orthodontic treatment as a tool for orthodontic diagnosis. Materials and Methods: Four hundred and fifty intraoral scan (IOS) images were selected from orthodontic patients seeking treatment in private orthodontic centers. Real inter-canine, inter-premolar, and inter-molar widths were measured digitally. Two of the main machine learning models were used: the Python programming language and machine learning algorithms, implementing the data on k-nearest neighbor and linear regression. Results: After the dataset had been implemented on the two ML algorithms, linear regression and k-nearest neighbor, the evaluation metric shows that KNN gives better prediction accuracy than LR does. The resulting accuracy was around 99%. Conclusions: it is possible to leverage machine learning to enhance orthodontic diagnosis and treatment planning by predicting linear dental arch measurements and preventing anterior segment malocclusion.
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spelling pubmed-106734362023-11-09 Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study Rauf, Aras Maruf Mahmood, Trefa Mohammed Ali Mohammed, Miran Hikmat Omer, Zana Qadir Kareem, Fadil Abdullah Medicina (Kaunas) Article Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to avoid future crowding in growing patients or even in adult patients seeking orthodontic treatment as a tool for orthodontic diagnosis. Materials and Methods: Four hundred and fifty intraoral scan (IOS) images were selected from orthodontic patients seeking treatment in private orthodontic centers. Real inter-canine, inter-premolar, and inter-molar widths were measured digitally. Two of the main machine learning models were used: the Python programming language and machine learning algorithms, implementing the data on k-nearest neighbor and linear regression. Results: After the dataset had been implemented on the two ML algorithms, linear regression and k-nearest neighbor, the evaluation metric shows that KNN gives better prediction accuracy than LR does. The resulting accuracy was around 99%. Conclusions: it is possible to leverage machine learning to enhance orthodontic diagnosis and treatment planning by predicting linear dental arch measurements and preventing anterior segment malocclusion. MDPI 2023-11-09 /pmc/articles/PMC10673436/ /pubmed/38004022 http://dx.doi.org/10.3390/medicina59111973 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
Rauf, Aras Maruf
Mahmood, Trefa Mohammed Ali
Mohammed, Miran Hikmat
Omer, Zana Qadir
Kareem, Fadil Abdullah
Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study
title Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study
title_full Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study
title_fullStr Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study
title_full_unstemmed Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study
title_short Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study
title_sort orthodontic implementation of machine learning algorithms for predicting some linear dental arch measurements and preventing anterior segment malocclusion: a prospective study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10673436/
https://www.ncbi.nlm.nih.gov/pubmed/38004022
http://dx.doi.org/10.3390/medicina59111973
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