ArticleInternational journal of legal medicine2021
Machine learning approaches for sex estimation using cranial measurements.
Article in International journal of legal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Sex estimation from morphometry of lumbar vertebrae (L4-L5): A machine learning approach.International journal of legal medicine · 2026Article
- Deep learning-based sex estimation from multi-planar cranial CT images in a Thai population.Anatomical science international · 2026Article
- Comprehensive assessment of minors' biological profiles using explainable machine learning: the assessment of age, stature, body mass, and sex.International journal of legal medicine · 2026Article
- Automated skull orientation classification in Thai population using CNN-based transfer learning.Scientific reports · 2026Article
- Evaluation of Sex-Related Morphometry of the Sella Turcica in Autopsy Using Machine Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Triticeal cartilage in forensic anthropological investigations: sex and stature estimation with a machine learning approach.International journal of legal medicine · 2026Article
- Coupling geometric morphometrics and machine learning for mandibular sex estimation in Late Pleistocene and Late Modern populations.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
- Skull CT metadata for automatic bone age assessment by using three-dimensional deep learning framework.International journal of legal medicine · 2025Article
- Evaluating the Effectiveness of Coxal Bone Measurements for Sex Estimation via Machine Learning.Biology · 2025Article
- Sexual dimorphism of the humerus bones in a French sample: comparison of several statistical models including machine learning models.International journal of legal medicine · 2025Article
- Sex estimation from patellar measurements in a contemporary Italian population: a machine learning approach.International journal of legal medicine · 2025Article
- Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation.International journal of legal medicine · 2025Review
- A statistical evaluation of the sexual dimorphism of the acetabulum in an Iberian population.International journal of legal medicine · 2025Article
- Developing a fully applicable machine learning (ML) based sex classification model using linear cranial dimensions.Scientific reports · 2024Article
- Sex Estimation Through Orbital Measurements: A Machine Learning Approach for Forensic Science.Diagnostics (Basel, Switzerland) · 2024Article
- Enhancing Sex Estimation Accuracy with Cranial Angle Measurements and Machine Learning.Biology · 2024Article
- Sex determination through maxillary dental arch and skeletal base measurements using machine learning.Head & face medicine · 2024Article
- Mandibular and dental measurements for sex determination using machine learning.Scientific reports · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
The aim of the present study is to apply support vector machines (SVM) and artificial neural network (ANN) as sex classifiers and to generate useful classification models for sex estimation based on cranial measurements. Besides, the performance of the generated sub-symbolic machine learning models is compared with models developed through logistic regression (LR). The study was carried out on computed tomography images of 393 Bulgarian adults (169 males and 224 females). The three-dimensional coordinates of 47 landmarks were acquired and used for calculation of the cranial measurements. A total of 64 measurements (linear distances, angles, triangle areas and heights) and 22 indices were calculated. Two datasets were assembled including the linear measurements only and all measurements and index, respectively. An additional third dataset comprising all possible interlandmark distances between the landmarks was constructed. Two machine learning algorithms-SVM and ANN and a traditional statistical analysis LR-were applied to generate models for sex estimation. In addition, two advanced attribute selection techniques (Weka BestFirst and Weka GeneticSearch) were used. The classification accuracy of the models was evaluated by means of 10 × 10-fold cross-validation procedure. All three methods achieved accuracy results higher than 95%. The best accuracy (96.1 ± 0.5%) was obtained by SVM and it was statistically significantly higher than the best results achieved by ANN and LR. SVM and ANN reached higher accuracy by training on the full datasets than the selection datasets, except for the sample described by the interlandmark distances, where the reduction of attributes by the GeneticSearch algorithm improved the accuracy.
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