Scientific Reports (Apr 2024)

Mandibular and dental measurements for sex determination using machine learning

  • Erika Calvano Küchler,
  • Christian Kirschneck,
  • Guido Artemio Marañón-Vásquez,
  • Ângela Graciela Deliga Schroder,
  • Flares Baratto-Filho,
  • Fábio Lourenço Romano,
  • Maria Bernadete Sasso Stuani,
  • Mírian Aiko Nakane Matsumoto,
  • Cristiano Miranda de Araujo

DOI
https://doi.org/10.1038/s41598-024-59556-9
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 9

Abstract

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Abstract The present study tested the combination of mandibular and dental dimensions for sex determination using machine learning. Lateral cephalograms and dental casts were used to obtain mandibular and mesio-distal permanent teeth dimensions, respectively. Univariate statistics was used for variables selection for the supervised machine learning model (alpha = 0.05). The following algorithms were trained: logistic regression, gradient boosting classifier, k-nearest neighbors, support vector machine, multilayer perceptron classifier, decision tree, and random forest classifier. A threefold cross-validation approach was adopted to validate each model. The areas under the curve (AUC) were computed, and ROC curves were constructed. Three mandibular-related measurements and eight dental size-related dimensions were used to train the machine learning models using data from 108 individuals. The mandibular ramus height and the lower first molar mesio-distal size exhibited the greatest predictive capability in most of the evaluated models. The accuracy of the models varied from 0.64 to 0.74 in the cross-validation stage, and from 0.58 to 0.79 when testing the data. The logistic regression model exhibited the highest performance (AUC = 0.84). Despite the limitations of this study, the results seem to show that the integration of mandibular and dental dimensions for sex prediction would be a promising approach, emphasizing the potential of machine learning techniques as valuable tools for this purpose.

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