Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features.

Autor: Basso IB; Postgraduate Program in Dentistry, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil., de Jesus Freitas PF; School of Dentistry, Tuiuti University of Paraná, Curitiba, Paraná, Brazil., Ferraz AX; Postgraduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Center for Artificial Intelligence in Health-NIAS, Curitiba, Paraná, Brazil., Borkovski AJ; Graduate Program in Dentistry, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil., Borkovski AL; Graduate Program in Dentistry, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil., Santos RS; Postgraduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Center for Artificial Intelligence in Health-NIAS, Curitiba, Paraná, Brazil., Rached RN; Postgraduate Program in Dentistry, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil., Küchler EC; Medical Faculty, Department of Orthodontics, University Hospital Bonn, Bonn, Germany., Schroder AGD; School of Dentistry, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Postgraduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Center for Artificial Intelligence in Health-NIAS, Curitiba, Paraná, Brazil., de Araujo CM; School of Dentistry, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Postgraduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Center for Artificial Intelligence in Health-NIAS, Curitiba, Paraná, Brazil., Guariza-Filho O; Postgraduate Program in Dentistry, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil.; Center for Artificial Intelligence in Health-NIAS, Curitiba, Paraná, Brazil.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Nov 15; Vol. 19 (11), pp. e0312824. Date of Electronic Publication: 2024 Nov 15 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0312824
Abstrakt: Characteristics of the mandible structures have been relevant in anthropological and forensic studies for sex prediction. This study aims to evaluate the coronoid process, condyle, and sigmoid notch patterns in sex prediction through supervised machine learning algorithms. Cephalometric radiographs from 410 dental records of patients were screened to investigate the morphology of the coronoid process, condyle, and sigmoid notch and the Co-Gn distance. The following machine learning algorithms were used to build the predictive models: Decision Tree, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Logistic Regression, Multilayer Perceptron Classifier, Random Forest Classifier, and Support Vector Machine (SVM). A 5-fold cross-validation approach was adopted to validate each model. Metrics such as area under the curve (AUC), accuracy, recall, precision, and F1 Score were calculated for each model, and ROC curves were constructed. All tested variables demonstrated statistical significance (p < 0.10) and were included in the construction of the predictive model. The Co-Gn variable stood out as the most important among the evaluated independent variables, showing greater relevance in three of the four algorithms used in assessing feature importance. In the analysis of the models' performance, the AUC ranged from 0.82 [95% CI = 0.72-0.93] to 0.66 [95% CI = 0.53-0.76] for the test data, and from 0.83 [95% CI = 0.80-0.87] to 0.71 [95% CI = 0.61-0.75] for cross-validation. The precision of the models ranged from 0.83 [95% CI = 0.75-0.91] to 0.68 [95% CI = 0.58-0.78] in the test phase, and from 0.78 [95% CI = 0.74-0.82] to 0.69 [95% CI = 0.65-0.75] in cross-validation. The SVM, KNN, and Gradient Boosting Classifier algorithms stood out with the highest AUC and precision values in both cross-validation and testing. The use of condyle, coronoid process, and sigmoid notch characteristics, in combination with supervised machine learning predictive models, shows potential for contributing to sex prediction based on morphometric bone characteristics, particularly regarding the distance between the condyle and gnathion. However, given the study's limitations, these findings should be interpreted with caution.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Basso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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