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

Alberto Del Real, Octavio Del Real, Sebastian Sardina, Rodrigo Oyonarte*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

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.

Original languageEnglish
Pages (from-to)102-111
Number of pages10
JournalKorean Journal of Orthodontics
Volume52
Issue number2
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Korean Association of Orthodontists.

Keywords

  • Computer algorithm
  • Decision tree
  • Extraction vs. non-extraction
  • Orthodontic Index

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