Análise métrica de mandíbulas para a diagnose sexual

Abstract

Introduction: Sexual diagnosis is a fundamental step in determining the biological profile of living individuals or cadavers. The mandible is one of the most dimorphic bones in the skull and resistant to decomposition, being relevant in determining the individual's sex. With technological advances, the usefulness and precision of 3D technology was realized, namely the Cone Beam Computed Tomography in the collection and metric analysis of bone structures. Objective: Digital metric analysis of sexual dimorphism in a sample of mandibles belonging to the National Institute of Legal Medicine and Forensic Sciences, IP.
Materials and Methods: Observational study of a sample of 33 mandibles, 14 female and 19 male. The bone pieces and personal belongings with code and gender were photographically registered. Data were collected using the Cone Beam Computed Tomography Planmeca ProMax® 3DClassic and measurement was done by using the Simplant Pro 17.01 software. Descriptive and inference statistical study was performed using IBM® SPSS® software version 26.01. The parameters defined were: maximum and minimum width of the mandibular ramus, condylar heights; coronoid process heights, mandibular symphysis heights, mandibular angle, bi-mental, bi-antegonial, bi-gonial and bi-condylar distances and maximal mandibular length. Denominated from A. to K., respectively. Results: There were statistically significant difference in the following parameters: coronoid process height, condyle height, maximum length of mandible and minimum width of mandibular ramus (p <0.05). In a multivariate statistical analysis, by the stepwise method, it was possible to identify the coronoid process height as the best sex predictor, accurately in 72.2% of cases. Allowing to differentiate female and masculine jaws at 64.3% and 78.9% accuracy, respectively. Conclusion: Presence of sexual dimorphism in four parameters. The coronoid process height is the most dimorphic parameter and the best sex predictor in the sample.

Date of Award	30 Jul 2021
Original language	Portuguese
Awarding Institution	Universidade Católica Portuguesa
Supervisor	André Correia (Supervisor) & Cristina Figueiredo (Co-Supervisor)