Analysis of the performance of machine learning and deep learning methods for sex estimation of infant individuals from the analysis of 2D images of the ilium

Abstract

Reducing the subjectivity of the methods used for biological profile estimation is, at present, a priority research line in forensic anthropology. To achieve this, artificial intelligence (AI) techniques can be a valuable tool yet to be exploited in this discipline. The goal of this study is to compare the effectiveness of different machine learning (ML) methods with the visual assessment of an expert to estimate the sex of infant skeletons from images of the ilium. Photographs of the ilium of 135 individuals, age between 5 months of gestation and 6 years, from the collection of identified infant skeletons of the University of Granada have been used, and classic ML and deep learning (DL) techniques have been applied to develop prediction algorithms. To assess their effectiveness, the results have been compared with those obtained by a forensic expert, who has estimated the sex from each photograph through direct observation and subjective assessment following the criteria described by Schutkowsky in 1993. The results show that the algorithms obtained using DL techniques offer an accuracy of 59%, very close to the 61% obtained by the expert, and 10 percentual points better than classic ML techniques. This study offers promising results and represents the first AI-based approach for estimating sex in infant individuals using photographs of the ilium.