The Efficacy of Deep Learning Model on Sex Estimation in Patellae for A Thai Population

Abstract

Objective: This study investigates the sexual dimorphism in the patellae to develop sex estimation equations and determine the accuracy using machine learning (ML) and deep learning (DL) classification models. Materials and Methods: The sample of 250 pairs of patellae from the Siriraj Anatomical and Anthropological Bone Research Center (Si-AABRC) were measured for eight parameters. The data were statistically analysed using logistic regression model alongside, ML and DL were used to predict the best classifiers in sex classification. Rather than traditional radiographic images, this paper tries a novel integration of photographic images. Results: The average values for each parameter were significantly larger in males than females (p < 0.05), suggesting the presence of sexual dimorphism within the patellae between each sex. The most dimorphic parameter was Transverse Diameter of Articular Facet (TDAF). The parameters in females showed no significant difference between left and right except for Breadth of the Medial Articular Facet (BMAF). However, in males a significant difference was observed for Maximum Height (MAXH), Transverse Diameter of Articular Facet (TDAF) and Breadth of the Lateral Articular Facet (BLAF). The logistic regression equation generated included the following parameters: MAXH (R), BLAF (L), and TDAF (L). The overall accuracy obtained for different sex estimation models ranged from 80%, 80% to 86% and 49.7% to 79.2% using logistic regression, ML and DL, respectively. Conclusion: The patellae can be utilized by forensic anthropologists in determining the sex of an unknown individual.