Sex determination through maxillary dental arch and skeletal base measurements using machine learning.

Autor: de Araujo CM; School of Dentistry, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Graduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Center for Artificial Intelligence in Health - NIAS, Curitiba, Paraná, Brazil., de Jesus Freitas PF; School of Dentistry, Tuiuti University of Paraná, Curitiba, Paraná, Brazil., Ferraz AX; Graduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Center for Artificial Intelligence in Health - NIAS, Curitiba, Paraná, Brazil., Quadras ICC; Graduate Program in Dentistry, Orthodontics, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil., Zeigelboim BS; Graduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil., Priolo Filho S; Graduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Graduate Program in Forensic Psychology, Tuiuti University of Paraná, Curitiba, Paraná, Brazil., Beisel-Memmert S; Department of Orthodontics, University Hospital Bonn, Medical Faculty, Welschnonnenstr. 17, 53111, Bonn, Germany., Schroder AGD; School of Dentistry, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Graduate Program in Human Communication Health, Tuiuti University of Paraná, Curitiba, Paraná, Brazil.; Center for Artificial Intelligence in Health - NIAS, Curitiba, Paraná, Brazil., Camargo ES; Graduate Program in Dentistry, Orthodontics, Pontifícia Universidade Católica do Paraná, Curitiba, Brazil., Küchler EC; Department of Orthodontics, University Hospital Bonn, Medical Faculty, Welschnonnenstr. 17, 53111, Bonn, Germany. Erika.Kuchler@ukbonn.de.
Jazyk: angličtina
Zdroj: Head & face medicine [Head Face Med] 2024 Aug 30; Vol. 20 (1), pp. 44. Date of Electronic Publication: 2024 Aug 30.
DOI: 10.1186/s13005-024-00446-w
Abstrakt: Background: Cranial, facial, nasal, and maxillary widths have been shown to be significantly affected by the individual's sex. The present study aims to use measurements of dental arch and maxillary skeletal base to determine sex, employing supervised machine learning.
Materials and Methods: Maxillary and mandibular tomographic examinations from 100 patients were analyzed to investigate the inter-premolar width, inter-molar width, maxillary width, inter-pterygoid width, nasal cavity width, nostril width, and maxillary length, obtained through Cone Beam Computed Tomography scans. The following machine learning algorithms were used to build the predictive models: Logistic Regression, Gradient Boosting Classifier, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron Classifier (MLP), Decision Tree, and Random Forest Classifier. A 10-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 Receiver Operating Characteristic (ROC) curves were constructed.
Results: Univariate analysis showed statistical significance (p < 0.10) for all skeletal and dental variables. Nostril width showed greater importance in two models, while Inter-molar width stood out among dental measurements. The models achieved accuracy values ranging from 0.75 to 0.85 on the test data. Logistic Regression, Random Forest, Decision Tree, and SVM models had the highest AUC values, with SVM showing the smallest disparity between cross-validation and test data for accuracy metrics.
Conclusion: Transverse dental arch and maxillary skeletal base measurements exhibited strong predictive capability, achieving high accuracy with machine learning methods. Among the evaluated models, the SVM algorithm exhibited the best performance. This indicates potential usefulness in forensic sex determination.
(© 2024. The Author(s).)
Databáze: MEDLINE
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