The role of the sacroiliac joint in sex estimation: Analysis of morphometry and variation types using machine learning techniques

Abstract

This study aimed to evaluate the potential of machine learning algorithms in sex estimation by going beyond the traditional two-dimensional (2D) measurements of the pelvic bone, predominantly preferred in sex prediction, by including measurement data in three-dimensional (3D) images. Measurements were performed on abdominal multidetector computed tomography (MDCT) images of 152 individuals (77 females, 75 males) aged 18–85. 3D-Slicer software was used for measurements on 2D and 3D images. In 2D images, sacroiliac joint surface area, the angle of the joint surface, the distance between the right and left joints, joint space measurements, and joint variation typing were performed. The distances from the sacroiliac joint to the apex of the contralateral linea terminalis and from the joint to the superior and inferior pubic symphysis were measured on 3D images. It was confirmed that sacroiliac joint space measurements were significantly higher in males than in females. Among the sacroiliac joint variations, 46% in males and 28% in females were the most common standard joint. A strong positive correlation was found between sacroiliac joint distance and the distance of the sacroiliac joint to the contralateral linea terminalis apex and the sacroiliac joint to the superior and inferior pubic symphysis. In this study, the support vector machine algorithm gave the most successful result compared to other algorithms, reaching 88% accuracy in sex estimation. We hope our study will guide the use of artificial intelligence on 3D images for forensic identification, especially in sex estimation.