ArticleScientific reports2024
Mandibular and dental measurements for sex determination using machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Application of machine learning for sex estimation using the first and second ribs in the Northern Thai population.Anatomy & cell biology · 2026Article
- Evaluating machine learning and neural network architectures for forensic sex estimation using mandibular ramus and notch features on panoramic radiographs.International journal of legal medicine · 2026Article
- Two-group interlandmark distance analysis for skeletal sex estimation using resampling and posterior probability contour plots.International journal of legal medicine · 2026Article
- Comparative Analysis of Third Molar Segmentation Performance Between Sexes Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- Dental age estimation by comparing Demirjian's method and machine learning in Southeast Brazilian youth.Forensic science, medicine, and pathology · 2026Article
- Investigating a potential association between agenesis of the third molars and variations in dental crown dimensions.PloS one · 2026Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- Development and validation of an age estimation model based on dental characteristics using panoramic radiographs.Scientific reports · 2025Article
- Age estimation of children and adolescents from mandibles using machine learning.Scientific reports · 2025Article
- Estimation of sexual dimorphism of adult human mandibles of South Indian origin using non-metric parameters and machine learning classification algorithms.Scientific reports · 2025Article
- Machine learning in sex estimation using CBCT morphometric measurements of canines.Clinical oral investigations · 2025Article
- Development and evaluation of a convolutional neural network model for sex prediction using cephalometric radiographs and cranial photographs.BMC medical imaging · 2025Article
- PITX2 expression and Neanderthal introgression in HS3ST3A1 contribute to variation in tooth dimensions in modern humans.Current biology : CB · 2025Article
- Sex estimation accuracy through metric evaluation of dental root: a cone-beam computed tomography-based study.Brazilian dental journal · 2025Article
- A new approach for sex prediction by evaluating mandibular arch and canine dimensions with machine-learning classifiers and intraoral scanners (a retrospective study).Scientific reports · 2024Article
- Sex determination through maxillary dental arch and skeletal base measurements using machine learning.Head & face medicine · 2024Article
- Artificial intelligence model for predicting sexual dimorphism through the hyoid bone in adult patients.PloS one · 2024Article
- Sex prediction through machine learning utilizing mandibular condyles, coronoid processes, and sigmoid notches features.PloS one · 2024Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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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