Akademik Veri Yönetim Sistemi
EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE, cilt.56, ss.1-9, 2025 (Scopus)
Abstract
Objective(s) Sex estimation is an important initial step for personal identification of unknown skeletal remains
in forensic examination. This study aims to determine sexual dimorphism in sternum measurements made with computed
tomography (CT) in the Turkish population. The study also aims to compare the effectiveness of various
machine learning techniques, including K-nearest neighbors (KNN), random forest, XGBoost, naive Bayes, logistic
regression, and linear discriminant analysis (LDA), in sex prediction, and to assess the usability of deep neural networks
(DNN) in sternum images.
Materials and methods CT images of 485 cases (248 males and 237 females) were used. From the sagittal plane
manubrial length (M) and corpus sternal length (CSL), from the coronal plane manubrial width (MW), sternal body
width at first sternebra (CSW1) and sternal body width at third sternebra (CSW3) were measured. From these measurements,
1 index (sternal index/SI), 1 length (corpus sterni length/CSL), and 1 area (sternal area/SA) were calculated. In
addition, sternum images in the sagittal and coronal planes were recorded to train the deep neural network.
Results The difference between male and female cases was significant for all measurements. The best-performing
model was LDA, followed by logistic regression, naïve Bayes, XGBoost, random forest, and KNN, respectively. The lowest
performance was observed in the DNN model using sternum images. However, there was no statistically significant
difference between the area under curves (AUCs) of DNN and LDA.
Conclusion This study shows that sternum measurements or images can be used for sex estimation in the Turkish
population.
Keywords Anthropology, Deep neural networks, Forensic science, Machine learning, Sternum, Sex estimation