Sex estimation from the sternum in Turkish population using various machine learning methods and deep neural networks

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.