Oral Issues in the Elderly

Nagaratnam, Nages; Nagaratnam, Kujan; Cheuk, Gary

doi:10.1007/978-3-319-33434-9_92

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…Therefore, the model of an artificial neural network for sex determination does not consider the changes that occur due to tooth wear and restorations, among other age-related factors. 38 This study demonstrated the possibility of identifying sexual dimorphism in permanent canines using various statistical methods. It should be noted that model learning in this study was conducted on 121 subjects, which is a relatively small sample size for neural networks that are normally able to process and conduct learning on samples with incomparably larger amounts of data.…”

Section: Discussionmentioning

confidence: 85%

Artificial neural network model for predicting sex using dental and orthodontic measurements

Anic-Milosevic,

Medancic,

Calusic-Sarac

et al. 2023

Korean J Orthod

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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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Section: Discussionmentioning

confidence: 85%