Validating AI for Gender Prediction Using Morphometric Analysis of the Mandible

This retrospective validity study evaluates the accuracy of artificial intelligence (AI) in determining gender from mandibular morphometric linear measurements. The study utilizes pre-existing Cone Beam Computed Tomography (CBCT) scans of adult Egyptian dental patients. After automatic segmentation of the mandible from these scans, a radiologist will manually perform measurements from certain anatomical points. These measurements will be the reference standard for the AI models. A three-dimensional deep learning model will be developed to perform two tasks: 1. To identify the anatomical points and make the specified linear measurements from these points on the segmented mandibles 2. To accurately predict gender based on these measurements. (Main Objective) The primary objective of this study is to evaluate the accuracy of machine learning algorithms in gender identification from linear morphometric measurements of the mandible. The known gender from patient records will serve as the reference standard. This study will assess the reliability of AI as an objective tool for gender determination for forensic purposes.

Inclusion Criteria: * Scans belonging to patients above or at the age of 18 years (≥ 18 years) * Scans that image the area of interest clearly (mandible) with a craniofacial or maxillomandibular field of view. * Good quality of the scan with no artefacts Exclusion Criteria: * CBCT scans with significant artifacts (e.g. metal artefact) affecting visualization of the mandible * Congenital craniofacial anomalies affecting the mandibular anatomy * Large pathologies that affect the mandible's dimensions and cause significant bone loss. * Trauma to the mandible