ArticleScientific reports2022
A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium.
Article in Scientific reports, 2022. 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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18 citing papers in PubMed, 66 citations in OpenAlex.
- Deep learning-based sex estimation from multi-planar cranial CT images in a Thai population.Anatomical science international · 2026Article
- Sex estimation from the mandible in 3D computed tomography images.International journal of legal medicine · 2026Article
- Radiographic femoral measurements for sex and species classification in dogs and cats using machine learning.Journal of veterinary science · 2026Article
- Machine learning algorithms and artificial neural networks for predicting schizophrenia using orbital parameters.Scientific reports · 2025Article
- Deep learning for gender estimation using hand radiographs: a comparative evaluation of CNN models.BMC medical imaging · 2025Article
- Assessing Sternal Dimensions for Sex Classification: Insights from a Greek Computed Tomography-Based Study.Diagnostics (Basel, Switzerland) · 2025Article
- Discriminant function analysis of maxillary bone measurements for sex estimation in a Colombian population by using cone-beam computed tomography.The Journal of forensic odonto-stomatology · 2025Article
- 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
- Human gender estimation from CT images of skull using deep feature selection and feature fusion.Scientific reports · 2024Article
- Mandibular and dental measurements for sex determination using machine learning.Scientific reports · 2024Article
- Forensic Gender Prediction by Using Mandibular Morphometric Indices: A Panoramic Radiograph Study.Cureus · 2024Article
- Sex estimation techniques based on skulls in forensic anthropology: A scoping review.PloS one · 2024Article
- Artificial intelligence model for predicting sexual dimorphism through the hyoid bone in adult patients.PloS one · 2024Article
- Machine Learning Models for Prediction of Sex Based on Lumbar Vertebral Morphometry.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence-based forensic sex determination of East Asian cadavers from skull morphology.Scientific reports · 2023Article
- A Virtual, 3D Multimodal Approach to Victim and Crime Scene Reconstruction.Diagnostics (Basel, Switzerland) · 2023Review
- The Accuracy of Sex Identification Using CBCT Morphometric Measurements of the Mandible, with Different Machine-Learning Algorithms-A Retrospective Study.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study is to test whether sex prediction can be made by using machine learning algorithms (ML) with parameters taken from computerized tomography (CT) images of cranium and mandible skeleton which are known to be dimorphic. CT images of the cranium skeletons of 150 men and 150 women were included in the study. 25 parameters determined were tested with different ML algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews correlation coefficient (Mcc) values were included as performance criteria and Minitab 17 package program was used in descriptive statistical analyses. p ≤ 0.05 value was considered as statistically significant. In ML algorithms, the highest prediction was found with 0.90 Acc, 0.80 Mcc, 0.90 Spe, 0.90 Sen, 0.90 F1 values as a result of LR algorithms. As a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. Acc ratios of other MLs were found to be between 0.81 and 0.88. It has been concluded that the LR algorithm to be applied to the parameters obtained from CT images of the cranium skeleton will predict sex with high accuracy.
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