RecruitingNot Applicable

Validating AI for Gender Prediction Using Morphometric Analysis of the Mandible

Egypt385 participantsStarted 2026-06-01

Plain-language summary

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.

Who can participate

Age range

18 Years

Sex

ALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

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

Questions worth asking your doctor

Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.

1Based on my diagnosis and history, is this trial worth exploring for me — or is there a standard treatment we should try first?
2What does this trial's phase tell us about how much is already known about its safety and benefit?
3What would taking part actually involve for me — visits, tests, time, and travel?
4What are the known and possible risks or side effects I should weigh, and how would they be monitored?
5If this trial isn't the right fit, what other options or trials would you suggest I look into?

Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.

Questions for the trial coordinator

The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.

1What does taking part actually involve week to week — how many visits, where, and how long does each one take?
2What costs are covered by the study, and what might I have to pay for myself, including travel, parking, or time off work?
3What happens during screening, and what happens if the study team confirms I don't meet the criteria after those tests?
4Who pays for the scans, blood work, and other tests the trial requires — the study, my insurance, or me?
5How will being in the trial affect my regular care, and will my own doctor stay informed and involved?
6Can I leave the trial at any point if I change my mind, and what would happen to my care if I do?

A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.

What they're measuring

Accuracy of AI in gender prediction

Timeframe: At study completion (at completion of analysis of the validation dataset) (12 months)

Trial details

NCT IDNCT07726290

SponsorCairo University

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2027-03-30

Contact for this trial

Ashrakat Abdelmonem Habib, Master's candidate

+201146752225 ashrakat.habib@dentistry.cu.edu.eg

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