Machine learning for the classification of surgical patients in orthodontics

Ferro-Sánchez, Carlos Andrés, Díaz-Laverde, Christian Orlando, Romero Cano, Victor ORCID: https://orcid.org/0000-0003-2910-5116, Campo, Oscar and González-Vargas, Andrés Mauricio 2024. Machine learning for the classification of surgical patients in orthodontics. Presented at: IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering, Florianópolis, Brazil, 24-28 October 2022. IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering. CLAIB CBEB 2022 2022. IFMBE Proceedings, vol 99. , vol.99 Cham: Springer, pp. 207-217. 10.1007/978-3-031-49404-8_21

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Abstract

Dentofacial anomalies, also known as malocclusions, are alterations with a congenital, traumatic, or growth origin. These anomalies can generate functional and aesthetic problems in those who suffer from them and have been reported by the World Health Organization as the third most prevalent oral disease. The most commonly used methods for correcting these anomalies are orthodontics and orthognathic surgery. The diagnosis, and the correct selection of the treatment to be carried out, are part of an extensive process that involves collecting different cephalometric and clinical data, and depend on the clinician’s experience. Therefore, no standardized process allows the classification or diagnosis among patients who achieve the best result with orthodontics, that is, non-surgical procedures or if surgical intervention is necessary. This study aims to propose a digital tool based on machine learning algorithms that may help the clinician to select an orthodontics or surgical treatment for patients who are about to start their treatment.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Springer
ISBN:	9783031494031
Date of First Compliant Deposit:	8 March 2024
Date of Acceptance:	1 December 2022
Last Modified:	11 Nov 2024 03:30
URI:	https://orca.cardiff.ac.uk/id/eprint/167062

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