Gender Prediction from Angular and Linear Parameters in Cranium Lateral View by using Machine Learning Algorithms: A Computed Tomography Study
DOI:
https://doi.org/10.6000/1929-6029.2025.14.51Keywords:
Artifical Neural Networks, Sex Estimation, Cranial Landmarks, Lateral Cranial View, Computed TomographyAbstract
The purpose of the study was to predict a gender by using Machine Learning Algorithms (MLA) with variables of the lateral view of the cranium from Computed Tomography (CT) images.
A total of 5 parameters (3 linear and 2 angular) of the lateral view of the cranium were evaluated on CT images of 200 female and 200 male adult individuals in the present study. These parameter measurements were analyzed with MLA and Logistic Regression (LR), Random Forest (RF), Linear Discriminant Analysis (LDA), K-Nearest Neighborhood (KNN) and Naive Bayes (NB) models were used. Accuracy (Acc), Sensitivity (Sen), Specificity (Spe) and F1 scores (F1) were used as the evaluation criteria in the study.
As a result of MLA, the Acc ratio was found to be 0.77 for the KNN algorithm, 0.84 in the NB algorithm, 0.85 in the LDA algorithm, 0.70 in the RF algorithm and 0.81 in the LR algorithm. As a result of the analysis, 0.85 Acc, 0.85 Sen, Spe 0.85 and 0.85 F1 values were found in the LDA algorithm with the highest accuracy. When the significance level of the variables in the study was examined, it was found that variable A had the best effect.
It was found that the MLA used for the variables of the lateral view of the cranium yielded high accuracy regarding gender and the LDA Model was effective in predicting gender.
References
Doğan Y, Aka PS. Authenticity of sex determination from mesiodistal and buccolingual tooth dimensions in forensic cases. Anthropology 2021; (41): 78-86. DOI: https://doi.org/10.33613/antropolojidergisi.907808
Gülhan Ö. Sex Estimation from the Pelvis Using Radiological Methods: Turkish Sample. Anthropology 2018; 36: 53-69.
Toneva D, Nikolova S, Agre G, Zlatareva D, Hadjidekov V, Lazarov N. Machine learning approaches for sex estimation using cranial measurements. International Journal of Legal Medicine 2021; 135: 951-966. DOI: https://doi.org/10.1007/s00414-020-02460-4
Ray A, Ray G, Kurtul I, et al. Sex estimation from the variables of talocrural joint by using machine learning algorithms. Journal of Forensic and Legal Medicine 2025; 114: 102912. DOI: https://doi.org/10.1016/j.jflm.2025.102912
Sertel Meyvacı S, Göller Bulut D, Öztürk AT, Ankaralı H. Gender estimation from angular parameters of mandible in Turkish adults. Med J West Black Sea 2021; 5(2): 240-7. DOI: https://doi.org/10.29058/mjwbs.873771
Sertel Meyvaci S, Ankarali H, Bulut DG, Taskin B. Performances of Different Classification Algorithms in Sex Determination from First Cervical Vertebra Measurements. International Journal of Morphology 2024; 42(5). DOI: https://doi.org/10.4067/S0717-95022024000501439
Du Jardin P, Ponsaillé J, Alunni-Perret V, Quatrehomme G. A comparison between neural network and other metric methods to determine sex from the upper femur in a modern French population. Forensic Sci Int 2009; 192: 127-126. DOI: https://doi.org/10.1016/j.forsciint.2009.07.014
Musilová B, Dupej J, Velemínská J, Chaumoitre K, Bruzek J. Exocranial surfaces for sex assessment of the human cranium. Forensic Science International 2016; 269: 70-77. DOI: https://doi.org/10.1016/j.forsciint.2016.11.006
Peiffer-Smadja N, Rawson TM, Ahmad R, Buchard A, Georgiou P, Lescure FX, Birgand G, Holmes AH. Machine learning for clinical decision support in infectious diseases: a narrative review of current applications. Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases 2020; 26(5): 584-595. DOI: https://doi.org/10.1016/j.cmi.2019.09.009
Borstelmann SM. Machine Learning Principles for Radiology Investigators. Academic Radiology 2020; 27(1): 13-25. DOI: https://doi.org/10.1016/j.acra.2019.07.030
Bertsatos A, Chovalopoulou ME, Brůžek J, Bejdová Š. Advanced procedures for skull sex estimation using sexually dimorphic morphometric features. International Journal of Legal Medicine 2020; 134(5): 1927-1937. DOI: https://doi.org/10.1007/s00414-020-02334-9
Gillet C, Costa-Mendes L, Rérolle C, Telmon N, Maret D, Savall F. Sex estimation in the cranium and mandible: a multislice computed tomography (MSCT) study using anthropometric and geometric morphometry methods. International Journal of Legal Medicine 2020; 134(2): 823-832. DOI: https://doi.org/10.1007/s00414-019-02203-0
Lopez-Capp TT, Rynn C, Wilkinson C, de Paiva LAS, Michel-Crosato E, Biazevic MGH. Sexing the cranium from the foramen magnum using discriminant analysis in a Brazilian sample. Brazilian Dental Journal 2018; 29(6): 592-598. DOI: https://doi.org/10.1590/0103-6440201802087
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