Sex determination through maxillary dental arch and skeletal base measurements using machine learning.
Artificial intelligence
Machine learning
Maxilla
Sex characteristics
Journal
Head & face medicine
ISSN: 1746-160X
Titre abrégé: Head Face Med
Pays: England
ID NLM: 101245792
Informations de publication
Date de publication:
30 Aug 2024
30 Aug 2024
Historique:
received:
29
04
2024
accepted:
12
08
2024
medline:
31
8
2024
pubmed:
31
8
2024
entrez:
30
8
2024
Statut:
epublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
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
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 39215305
doi: 10.1186/s13005-024-00446-w
pii: 10.1186/s13005-024-00446-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
44Informations de copyright
© 2024. The Author(s).
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