LAUSR

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 analyzed retrospectively. The images in Digital Imaging and Communications in Medicine (DICOM) format were transferred to personal workstation Radiant DICOM Viewer program. Length and width measurements of the first and fifth metatarsal and foot phalanx bones were performed on the transferred images. In addition, the ratios of the total length of the first proximal and distal phalanx and length of the first metatarsal and total length of fifth proximal, middle, and distal phalanx and maximum length of fifth metatarsal were calculated. RESULTS As a result of machine learning (ML) algorithms, highest accuracy (Acc), specificity (Spe), sensitivity (Sen), Matthews correlation coefficient (Mcc) values were found as 0.85, 0.86, 0.85 and 0.71, respectively with Decision Tree algorithm. It was found that accuracy rates of other algorithms varied between 0.74 and 0.83. CONCLUSIONS As a result of our study, it was found that sex estimation was made with high accuracy rate by using ML algorithms on X-ray images of the first and fifth metatarsal and foot phalanx. We think that in cases when pelvis, cranium and long bones are harmed and examination is difficult, bones of the first and fifth metatarsal and foot phalanx can be used for sex estimation.