Validating AI for Gender Prediction Using Morphometric Analysis of the Mandible

The Accuracy of Artificial Intelligence in Gender Prediction Using Morphometric Analysis of the Mandible - a Validity Study

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.

Study Overview

• Status: Recruiting
• Conditions: Gender; MANDIBLE; Artifical Intelligence; Forensic Dentistry; Morphometric Analysis; Gender Prediction

Detailed Description

Study Overview and Objective:

1. To evaluate the accuracy of machine learning algorithms (ANN and logistic regression) in gender identification from linear morphometric measurements of the mandible. These measurements will be derived from automated mandibular segmentations from CBCT scans of a sample of Egyptian dental patients. The AI model's accuracy, precision, and sensitivity (recall) will be assessed. The model's predictions will be compared to the known gender from patient records.
2. To evaluate the accuracy of AI (3D UNet) in anatomical point identification and linear measurement. The model's performance will be compared to the radiologist's manual point placement and measurements.

Rationale:

Gender identification is vital in forensic odontology as it makes individual identification easier and faster. Artificial Intelligence can improve accuracy, speed, and efficiency in gender identification processes. The mandible is a sturdy bone with dimorphic features that can aid in gender determination even if other bones are lost. Cone beam computed tomography (CBCT) provides accurate 3D images and therefore accurate measurements. Linear morphometric measurements of the mandible derived from CBCT will provide an objective way of gender determination. AI algorithms can use these measurements to predict gender. Evaluating the accuracy of artificial intelligence in gender determination using morphometric measurements from CBCT-based mandibular segmentations is therefore essential to assess its reliability and potential value as an objective diagnostic tool in forensic practice.

Data Source and Population: Pre-existing, anonymized CBCT scans will be retrieved from the archives of the Oral and Maxillofacial Radiology clinic at the Faculty of Dentistry, Cairo University.

Sample Size Calculation The sample size was calculated to be 385 CBCT DICOMs using the Stats Kingdom statistical tool, based on the primary outcome - accuracy of AI in gender prediction. The power of study was 0.8, the margin of error was 0.04 and the alpha level of significance was 0.05.

Methodology:

Reference standard:

After recruiting a scan in the study, the name of the patient will be removed, and the scan will be given an identification number instead. One of the supervisors will have a dataset with the identification number, age, and the correct gender written before all personal information is erased from the CBCT scan. This will ensure that the radiologist performing the morphometric measurements will be blinded.

The DICOM file will be opened in 3D slicer imaging software (version 5.10.0; MIT, Cambridge, MA, USA) where automatic segmentation of the mandible will be peformed.

The radiologist performing the measurements has 2 years of experience. The following linear measurements will be performed: bi-coronoid width, bi-condylar width, distance from right gonion to menton and left gonion to menton, and distance from right coronoid to right gonion and left coronoid to left gonion.These measurements will be written in a separate dataset with the identification number of the scan with no information about the gender. Interobserver agreement will be validated by a senior radiologist (>15 years experience) who will re-evaluate a 30% sample of the cases. The datasets will be sent to a statistician to assess the normality of data and sexual dimorphism.

The segmented mandibles (NRRD files) and the measurement files (JSON files) will be saved for each case to be introduced in the AI model by the AI engineer.

Index test:

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 The AI engineer will be responsible for model implementation. For the first task, 3D U-Net model will be employed. The NRRD and JSON files will be introduced to the model for training and testing. For the second task, Artificial neural networks and logistic regression will be used.

Data preprocessing will be performed and the dataset will be divided into training, validation, and testing subsets to ensure balanced and unbiased evaluation. K-fold cross-validation will be used if sample size is small.

Training:

The landmark detection model will be trained using supervised learning techniques .The classification models will subsequently be trained using the extracted feature representations. Each model will be trained using the same training data and preprocessing pipeline.

Testing:

In the testing phase, the trained system will process unseen segmented mandibles to automatically localize keypoints, compute relevant geometric features, and generate gender predictions without further model updates.

Evaluation and Expected Outcome:

Model performance will be evaluated using quantitative localization error metrics for keypoint detection and standard classification metrics for gender prediction.

Statistical Analysis:

Statistical tests will be applied to assess the normality of data and sexual dimorphism of the linear morphometric mandibular measurements before introducing these measurements to the AI model. Mean Euclidean Distance Error (in millimeters), and Root Mean Square Error (RMSE) will be used to assess the accuracy of the model in anatomical point placement in comparison to the radiologist's measurements. Accuracy, Precision, Recall (sensitivity), Specificity, F1-score, and a Confusion matrix will be used to evaluate the prediction models.

No data will be missed considering the retrospective nature of the study

• Study Type: Observational
• Enrollment (Estimated): 385

Contacts and Locations

• Study Contact: Name: Ashrakat Abdelmonem Habib, Master's Candidate; Phone Number: +201146752225; Email: ashrakat.habib@dentistry.cu.edu.eg

Study Locations

• Egypt
  • Cairo, Egypt
    • Recruiting
    • Faculty of Dentistry, Cairo University
    • Contact: Ashrakat Abdelmonem Habib, Master's Candidate; Phone Number: +201146752225; Email: ashrakat.habib@dentistry.cu.edu.eg

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of AI in anatomical point placement and linear measurements	Landmark placement and linear measurments of 3D UNet model versus radiologists' manual measurements will be compared. The measuring unit will be in millimeters (mm). Mean Euclidean Distance Error (in millimeters), and Root Mean Square Error (RMSE) will be used to assess the accuracy of the model in anatomical point placement in comparison to the radiologist's measurements	At study completion (at completion of analysis of the validation dataset) (12 months)

Collaborators and Investigators

• Sponsor: Cairo University
• Investigators: Study Director: Ola Mohamed Associate professor, Oral and Maxillofacial Radiology, Faculty of Dentistry, Cairo University

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2026
• Primary Completion (Estimated): March 30, 2027
• Study Completion (Estimated): May 1, 2027

Study Registration Dates

• First Submitted: July 19, 2026
• First Submitted That Met QC Criteria: July 22, 2026
• First Posted (Actual): July 24, 2026

Study Record Updates

• Last Update Posted (Actual): July 24, 2026
• Last Update Submitted That Met QC Criteria: July 22, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Keywords: machine learning; gender determination; artifical intelligence; gender prediction; morphometric analysis of the mandible; mandible dimorphism; linear measurements of the mandible
• Additional Relevant MeSH Terms: Behavior; Sexual Behavior; Coitus
• Other Study ID Numbers: OMFR 8-1-2 (2026)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

IPD Plan Description

Individual participant data will not be made publicly available to protect patient privacy and adhere to institutional ethical guidelines.

All personal information will be removed, and the CBCT scans will be identified by an identification number. Publication of results will not reveal patient identity or allow identification of individuals.

De-identified morphometric mandibular measurements and corresponding gender classifications may be made available upon reasonable request.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No