Automated Sex Assessment of Individual Adult Tooth X-Ray Images

Abstract

Sex assessment is an important step of the forensic process. Dental remains are often the only remains left to examine due to their resistance to decay and external factors. Contemporary forensic odontology literature describes multiple methods for sex assessment from mandibular parameters, all of which require manual measurements and expert training. This study aims to explore the applicability of deep learning and image analysis methods to automate this task, thus allowing for easier reproducibility of assessments, reduction of the time experts lose on repetitive tasks, and potentially better performance. We have evaluated state-of-the-art deep learning models and components on the largest dataset of individual adult tooth x-ray images, consisting of 76293 samples. This study also explores the usage of decayed or structurally altered teeth, with which contemporary methods struggle. Two types of models are constructed, a family of models specialized for specific tooth types, and a general model that can assess the sex from any tooth type. We examine the performance of those models per tooth type and age group, as well as the impact of decayed and structurally altered teeth. The specialized models achieve an overall accuracy of 72.40%, and the general model reaches an overall accuracy of 72.68%.