2023
Hernández, G.; González-Briones, A.; Machado, J.; Chamoso, P.; Novais, P.
A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents Proceedings Article
Em: C., Bonsangue M. M. Anutariya (Ed.): pp. 300-313, Springer Science and Business Media Deutschland GmbH, 2023, ISSN: 18650929, (cited By 0; Conference of 1st International Conference on Data Science and Artificial Intelligence, DSAI 2023 ; Conference Date: 27 November 2023 Through 29 November 2023; Conference Code:304419).
Resumo | Links | BibTeX | Etiquetas: Craniofacial; Craniofacial morphological growth; Deep cavity; Dental extraction; Machine learning approaches; Machine learning techniques; Machine-learning; Morphological patterns; Orthodontic treatments; Tooth extraction, Decision making; Dentistry; Learning algorithms; Machine learning, Extraction
@inproceedings{Hernández2023300,
title = {A Machine Learning Approach to Evaluating the Relationship Between Dental Extraction and Craniofacial Growth in Adolescents},
author = {G. Hernández and A. González-Briones and J. Machado and P. Chamoso and P. Novais},
editor = {Bonsangue M. M. Anutariya C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177874390&doi=10.1007%2f978-981-99-7969-1_22&partnerID=40&md5=97e552640b738ab3e2668bc83b3e3e02},
doi = {10.1007/978-981-99-7969-1_22},
issn = {18650929},
year = {2023},
date = {2023-01-01},
journal = {Communications in Computer and Information Science},
volume = {1942 CCIS},
pages = {300-313},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {There may be multiple reasons for tooth extraction, such as deep cavities, an infection that has destroyed an important portion of the tooth or the bone that surrounds it, or for orthodontic reasons, such as the lack of space for all the teeth in the mouth. In the case of orthodontics, however, there is a relationship between tooth extraction and the craniofacial morphological pattern. The purpose of this study is to establish whether such a relationship exists in adolescents and to evaluate it and to serve as a tool to support medical decision making. Machine Learning techniques can now be applied to datasets to discover relationships between different variables. Thus, this study involves the application of a series of Machine Learning techniques to a dataset containing information on orthodontic tooth extraction in adolescents. It has been discovered that by following simple rules it is possible to identify the need of treatment in 98.7% of the cases, while the remaining can be regarded as “limited cases”, in which an expert’s opinion is necessary. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.},
note = {cited By 0; Conference of 1st International Conference on Data Science and Artificial Intelligence, DSAI 2023 ; Conference Date: 27 November 2023 Through 29 November 2023; Conference Code:304419},
keywords = {Craniofacial; Craniofacial morphological growth; Deep cavity; Dental extraction; Machine learning approaches; Machine learning techniques; Machine-learning; Morphological patterns; Orthodontic treatments; Tooth extraction, Decision making; Dentistry; Learning algorithms; Machine learning, Extraction},
pubstate = {published},
tppubtype = {inproceedings}
}
There may be multiple reasons for tooth extraction, such as deep cavities, an infection that has destroyed an important portion of the tooth or the bone that surrounds it, or for orthodontic reasons, such as the lack of space for all the teeth in the mouth. In the case of orthodontics, however, there is a relationship between tooth extraction and the craniofacial morphological pattern. The purpose of this study is to establish whether such a relationship exists in adolescents and to evaluate it and to serve as a tool to support medical decision making. Machine Learning techniques can now be applied to datasets to discover relationships between different variables. Thus, this study involves the application of a series of Machine Learning techniques to a dataset containing information on orthodontic tooth extraction in adolescents. It has been discovered that by following simple rules it is possible to identify the need of treatment in 98.7% of the cases, while the remaining can be regarded as “limited cases”, in which an expert’s opinion is necessary. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.