Three-Dimensional Dental Analysis for Sex Estimation in the Italian Population: A Pilot Study Based on a Geometric Morphometric and Artificial Neural Network Approach

Abstract

Dental dimorphism can be used for discriminating sex in forensic contexts. Geometric morphometric analysis (GMA) allows the evaluation of the shape and size, separately, of uneven 3D objects. This study presents experiments using a novel combination of GMA and an artificial neural network (ANN) for sex classification, applied to premolars of Caucasian Italian adults (50 females and 50 males). General Procrustes superimposition (GPS) and the partial least square (PLS) method were performed, respectively, to study the shape variance between sexes and to eliminate landmark variations. The “set-aside” approach was used to assess the accuracy of the proposed neural networks. As the main findings of the pilot study, the proposed method applied to the first upper premolar correctly classified 90% of females and 73% of males of the test sample. The accuracy was 0.84 and 0.80 for the training and test samples, respectively. The sexual dimorphism resulting from GMA was low, although statistically significant. GMA combined with the ANN demonstrated better sex classification ability than previous odontometric or dental morphometric methods. Future research could overcome some limitations by considering a larger sample of subjects and other kinds of teeth and experimenting with the use of computer vision for automatic landmark positioning.

Proposal for Introducing a Digital Aesthetic Dentistry Course in Undergraduate Program: Contents and Ways of Administration

“…This makes it possible to understand the features of the face and helps, in part, to control aesthetic features [7][8][9][10]. A versatile tool to improve the diagnosis and design of aesthetic dental restorations is the Digital Smile Design (DSD) technique, which allows clinicians to make an appropriate analysis of the patient's initial situation and address critical diagnostic points [11].…”

A Semi-Automatic Method on a Small Italian Sample for Estimating Sex Based on the Shape of the Crown of the Maxillary Posterior Teeth

“…There is shared agreement in the previous scientific evidence that the two-dimensional measurements of metric methods have some major limitations in terms of their forensic applications since the accuracy of these methods considerably decreases when incomplete dentition is analysed and the obtained results are not satisfactorily consistent to suggest the use of odontometrics as unique tool for sex estimation, whilst morphological methods can offer only a qualitative evaluation largely affected by the operator’s subjectivity and the variability of dental anomalies [ 1 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

“…Therefore, our research aims to test techniques which are able to quantify the three-dimensional morphological characteristics of teeth. Some novel studies have considered the geometric morphometric analysis (GMA) [ 19 , 25 , 30 , 31 , 32 ], which allows the analytical study of the three-dimensional dental crown surfaces based on fixed landmarks and sliding semi-landmarks, quantifying the morphological characteristics and separately measuring both the shape and size differences between the sexes. Especially in archaeological fields, dental evidence is easily available even in the presence of largely compromised skeletal remains, and greater attention has been paid to identification methods based on geometric morphometric analysis [ 6 ].…”

“…To cope with these relevant limitations of the applicability of GMA, Oliva et al, 2021 [ 30 ] developed and validated a promising sex classification method based on the combination of GMA with artificial intelligence systems (artificial neural networks—ANNs). The pilot method [ 30 ] allowed a correct classification of 80% of the test sample analysing the crown morphology exclusively of the first upper left first premolar.…”

“…To cope with these relevant limitations of the applicability of GMA, Oliva et al, 2021 [ 30 ] developed and validated a promising sex classification method based on the combination of GMA with artificial intelligence systems (artificial neural networks—ANNs). The pilot method [ 30 ] allowed a correct classification of 80% of the test sample analysing the crown morphology exclusively of the first upper left first premolar. These results were more reliable than those of Yong’s study, which applied GMA not combined with ANN with an accuracy mainly below 70% and concluded that the occlusal shapes of male and female premolars are not endowed with significant sexual dimorphism [ 25 ].…”