A new approach for sex prediction by evaluating mandibular arch and canine dimensions with machine-learning classifiers and intraoral scanners (a retrospective study)

Abstract

In circumstances where antemortem information concerning the deceased individual is unavailable, forensic experts prepare biological profiling for unidentified human remains that aids in narrowing the search for identity. Biological profiling includes basic demographic information such as sex, age, stature, and ethnicity. Sex identification is the first and key step in the biological profiling of unidentified human remains, as it effectively reduces potential matches by excluding nearly one-half of the suspected cases and facilitates the subsequent stages. This study was conducted to assess the accuracy of artificial intelligence (AI) in predicting sex by analysing mandibular canine dimensions, mandibular intercanine distance (MICD), and mandibular canine index (MCI) obtained from three-dimensional (3D) digital impressions captured by using an intraoral scanner (IOS). The results of the receiver operating characteristic (ROC) test indicated that mean mandibular canine width (MeanMCW) had the highest sexual dimorphism with the area under the curve (AUC) of 0.912, and the Gaussian Naive Bayes (GNB) classifier demonstrated the highest testing accuracy among all machine learning (ML) models, achieving an accuracy of 92.5%. While the outcomes of this study are promising, further studies are imperative to validate these findings with larger sample sizes in different ethnic populations.