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Development of an Artificial Intelligence-Based Clinical Image Model for Detection, Classification, and Management Recommendations of Anterior Gingival Recession

NCT07693322 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 149

Last updated 2026-07-09

No results posted yet for this study

Summary

This study aims to develop and evaluate an artificial intelligence-based clinical image model for the detection, classification, and management recommendations of anterior gingival recession. The study will utilize clinical images of patients presenting with gingival recession to train and validate a machine learning model capable of accurately identifying and classifying the condition according to established clinical criteria. In addition, the model will provide preliminary treatment recommendations based on the severity and type of recession. This is a diagnostic and model-development study designed to support clinicians in improving the accuracy and consistency of diagnosis and treatment planning for gingival recession in the anterior region.

Conditions

  • Gingival Recessions

Interventions

DIAGNOSTIC_TEST

Artificial Intelligence-Based Clinical Image Analysis Model

An artificial intelligence-based clinical image model will be developed and evaluated using standardized clinical photographs of anterior teeth presenting with gingival recession. The model will be trained to detect the presence of gingival recession, classify lesions according to the Cairo classification system (RT1, RT2, and RT3), and generate preliminary management recommendations based on the identified classification. The system's performance will be assessed by comparing its diagnostic and classification outputs with expert clinical assessments.

Sponsors & Collaborators

  • Al-Azhar University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-06-15
Primary Completion
2026-01-15
Completion
2026-04-15

Countries

  • Egypt

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07693322 on ClinicalTrials.gov