Use of automated artificial intelligence to predict the need for orthodontic extractions

Del Real, Alberto J.; Del Real, Octavio; Sardina, Sebastian; Oyonarte Weldt, Rodrigo

Abstract

Objective: To develop and explore the usefulness of an artificial intelligence system for the prediction of the need for dental extractions during orthodontic treatments based on gender, model variables, and cephalometric records. Methods: The gender, model variables, and radiographic records of 214 patients were obtained from an anonymized data bank containing 314 cases treated by two experienced orthodontists. The data were processed using an automated machine learning software (Auto-WEKA) and used to predict the need for extractions. Results: By generating and comparing several prediction models, an accuracy of 93.9% was achieved for determining whether extraction is required or not based on the model and radiographic data. When only model variables were used, an accuracy of 87.4% was attained, whereas a 72.7% accuracy was achieved if only cephalometric information was used. Conclusions: The use of an automated machine learning system allows the generation of orthodontic extraction prediction models. The accuracy of the optimal extraction prediction models increases with the combination of model and cephalometric data for the analytical process.

Más información

Título según WOS: Use of automated artificial intelligence to predict the need for orthodontic extractions
Título según SCOPUS: ID SCOPUS_ID:85127917456 Not found in local SCOPUS DB
Título de la Revista: Korean #Journal of Orthodontics
Volumen: 52
Fecha de publicación: 2022
Página de inicio: 102
Página final: 111
DOI:

10.4041/KJOD.2022.52.2.102

Notas: ISI, SCOPUS