대한치과교정학회지 (The korean journal of orthodontics)

  • 제52권2호
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  • Pages.102-111
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  • 2022
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  • 2234-7518(pISSN)
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  • 2005-372X(eISSN)
대한치과교정학회 (The Korean Association Of Orthodontists)
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Use of automated artificial intelligence to predict the need for orthodontic extractions

  • Real, Alberto Del (Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes) ;
  • Real, Octavio Del (Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes) ;
  • Sardina, Sebastian (Department of Computer Science, School of Computing Technologies, RMIT University) ;
  • Oyonarte, Rodrigo (Graduate Orthodontic Program, Discipline of Orthodontics, Faculty of Odontology, Universidad de los Andes)
  • 투고 : 2021.05.11
  • 심사 : 2021.10.13
  • 발행 : 2022.03.25
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초록

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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참고문헌

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