Estimation of sex from femoral bone using radiological imaging methods in Turkish population.

Sex estimation is leading to determine the biological profile in forensic medicine. The aim of this study is to research the effectiveness of logistic regression (LogR) and discriminant function analysis (DFA) to create sex estimation models on femur images obtained with Computed Tomography (CT) angiography and to address the differences of femur, which show sexual dimorphism, among populations. All parameters were measured on three planes by adjusting the 300 CT angiography images from 150 women and 150 men that focused on the left femur to the orthogonal plane with standard magnification.

Sex prediction with morphometric measurements of first and fifth metatarsal and phalanx obtained from X-ray images by using machine learning algorithms.

Background: The aim of this study is to predict sex with machine learning (ML) algorithms by making morphometric measurements on radiological images of the first and fifth metatarsal and phalanx bones.

Materials And Methods: In this study, radiologic images of 263 individuals (135 female, 128 male) between the ages of 27 and 60 were analysed retrospectively. The images in digital imaging and communications in medicine (DICOM) format were transferred to personal workstation Radiant DICOM Viewer programme.