Validating AI for Gender Prediction Using Morphometric Analysis of the Mandible

NCT07726290 | Recruiting

• 1 other identifier: OMFR 8-1-2 (2026)
• Study Type: observational
• Target: 385
• Locations: 1 country
• Sites: 1

Brief 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. 1.To identify the anatomical points and make the specified linear measurements from these points on the segmented mandibles
2. 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.

Conditions

Gender; MANDIBLE; Forensic Dentistry; Gender Prediction; Morphometric Analysis; Artifical Intelligence

Keywords

gender prediction; gender determination; morphometric analysis of the mandible; mandible dimorphism; artifical intelligence; machine learning; linear measurements of the mandible

Outcome Measures

Primary Outcomes (1)

• Accuracy of AI in gender prediction

  The accuracy of machine learning models (Artificial Neural Networks and Logistic Regression) in gender prediction. The predictions results are compared to the known gender from patient records which serve as the reference standard. The measuring unit is categorical as male or female. Accuracy, Precision, Recall (sensitivity), Specificity, F1-score, and a Confusion matrix will be used to evaluate the prediction models.

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

Secondary Outcomes (1)

• Accuracy of AI in anatomical point placement and linear measurements

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

Study Arms (1)

Pre-existing CBCT scans from adult Egyptian dental patients showing the mandible.

All scans within this cohort undergo the same automatic segmentation followed by expert landmark placement and measurement taking. Subsequently, these segmentations will be introduced to the AI model for automated landmark placement, measurements and finally gender prediction based on these measurements.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

CBCT scans of patients, who have already been imaged for other treatment purposes, will be retrieved. Automatically segmented mandibles from these CBCT scans will be the population of the study.

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Cairo University: lead

Study Sites (1)

Faculty of Dentistry, Cairo University

Cairo, Egypt

RECRUITING

MeSH Terms

Conditions

Coitus

Condition Hierarchy (Ancestors)

Sexual Behavior; Behavior

Study Officials

• Ola Mohamed Associate professor

  Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University

  STUDY DIRECTOR

Central Study Contacts

Ashrakat Abdelmonem Habib, Master's candidate

CONTACT

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

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: July 19, 2026
• First Posted: July 24, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): March 30, 2027
• Study Completion (Estimated): May 1, 2027
• Last Updated: July 24, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

EG (1)