대한치과교정학회지 (The korean journal of orthodontics)

  • 제53권3호
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  • Pages.194-204
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  • 2023
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  • 2234-7518(pISSN)
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  • 2005-372X(eISSN)
대한치과교정학회 (The Korean Association Of Orthodontists)
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Artificial neural network model for predicting sex using dental and orthodontic measurements

  • Sandra Anic-Milosevic (Department of Orthodontics, School of Dental Medicine, University of Zagreb) ;
  • Natasa Medancic (Private Practice Policlinic IMED) ;
  • Martina Calusic-Sarac (Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek) ;
  • Jelena Dumancic (Department of Dental Anthropology, School of Dental Medicine, University of Zagreb) ;
  • Hrvoje Brkic (Department of Dental Anthropology, School of Dental Medicine, University of Zagreb)
  • 투고 : 2022.11.24
  • 심사 : 2023.04.25
  • 발행 : 2023.05.25
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초록

Objective: To investigate sex-specific correlations between the dimensions of permanent canines and the anterior Bolton ratio and to construct a statistical model capable of identifying the sex of an unknown subject. Methods: Odontometric data were collected from 121 plaster study models derived from Caucasian orthodontic patients aged 12-17 years at the pretreatment stage by measuring the dimensions of the permanent canines and Bolton's anterior ratio. Sixteen variables were collected for each subject: 12 dimensions of the permanent canines, sex, age, anterior Bolton ratio, and Angle's classification. Data were analyzed using inferential statistics, principal component analysis, and artificial neural network modeling. Results: Sex-specific differences were identified in all odontometric variables, and an artificial neural network model was prepared that used odontometric variables for predicting the sex of the participants with an accuracy of > 80%. This model can be applied for forensic purposes, and its accuracy can be further improved by adding data collected from new subjects or adding new variables for existing subjects. The improvement in the accuracy of the model was demonstrated by an increase in the percentage of accurate predictions from 72.0-78.1% to 77.8-85.7% after the anterior Bolton ratio and age were added. Conclusions: The described artificial neural network model combines forensic dentistry and orthodontics to improve subject recognition by expanding the initial space of odontometric variables and adding orthodontic parameters.

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과제정보

This research was funded by the Croatian Science Foundation under the project IP-2020-02-9423 - Tooth analysis in forensic and archaeological research.

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