(Del Real Alberto) - Universidad de los Andes Faculty of Odontology Discipline of Orthodontics
(Del Real Octavio) - Universidad de los Andes Faculty of Odontology Discipline of Orthodontics
(Sardina Sebastian) - RMIT University School of Computing Technologies Department of Computer Science
(Oyonarte Rodrigo) - Universidad de los Andes Faculty of Odontology Discipline of Orthodontics
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.
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Extraction vs. non-extraction, Computer algorithm, Decision tree, Orthodontic Index
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This study is the first to explore the performance of an AutoML system for the generation of predictive models for dental extractions for orthodontic treatments using sample sizes comparable to those used for traditional ML systems in the past; Three different models for the prediction of the orthodontic need of dental extractions were generated and tested using an AutoML method, and they achieved accuracies of up to 93.9% for predicting the need for tooth extractions, similar to those obtained by more complex methods.