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    Sex Estimation From Measurements of the Mastoid Triangle and Volume of the Mastoid Air Cell System Using Classical and Machine Learning Methods
    (Lippincott Williams & Wilkins, 2024) Sasani, Hadi; Etli, Yasin; Tastekin, Burak; Hekimoglu, Yavuz; Keskin, Siddik; Asirdizer, Mahmut
    Previous studies on the sexual dimorphism of the mastoid triangle have typically focused on linear and area measurements. No studies in the literature have used mastoid air cell system volume measurements for direct anthropological or forensic sex determination. The aims of this study were to investigate the applicability of mastoid air cell system volume measurements and mastoid triangle measurements separately and combined for sex estimation, and to determine the accuracy of sex estimation rates using machine learning algorithms and discriminant function analysis of these data. On 200 computed tomography images, the distances constituting the edges of the mastoid triangle were measured, and the area was calculated using these measurements. A region-growing algorithm was used to determine the volume of the mastoid air cell system. The univariate sex determination accuracy was calculated for all parameters. Stepwise discriminant function analysis was performed for sex estimation. Multiple machine learning methods have also been used. All measurements of the mastoid triangle and volumes of the mastoid air cell system were higher in males than in females. The accurate sex estimation rate was determined to be 79.5% using stepwise discriminant function analysis and 88.5% using machine learning methods.
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    Sex Estimation From the Paranasal Sinus Volumes Using Semiautomatic Segmentation, Discriminant Analyses, and Machine Learning Algorithms
    (Lippincott Williams & Wilkins, 2023) Hekimoglu, Yavuz; Sasani, Hadi; Etli, Yasin; Keskin, Siddik; Tastekin, Burak; Asirdizer, Mahmut
    The aims of this study were to determine whether paranasal sinus volumetric measurements differ according to sex, age group, and right-left side and to determine the rate of sexual dimorphism using discriminant function analysis and machine learning algorithms. The study included paranasal computed tomography images of 100 live individuals of known sex and age. The paranasal sinuses were marked using semiautomatic segmentation and their volumes and densities were measured. Sex determination using discriminant analyses and machine learning algorithms was performed. Males had higher mean volumes of all paranasal sinuses than females (P < 0.05); however, there were no statistically significant differences between age groups or sides (P > 0.05). The paranasal sinus volumes of females were more dysmorphic during sex determination. The frontal sinus volume had the highest accuracy, whereas the sphenoid sinus volume was the least dysmorphic. In this study, although there was moderate sexual dimorphism in paranasal sinus volumes, the use of machine learning methods increased the accuracy of sex estimation. We believe that sex estimation rates will be significantly higher in future studies that combine linear measurements, volumetric measurements, and machine-learning algorithms.