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

NCT07693322 | Completed

• 1 other identifier: OMPDR-108-1r
• Study Type: observational
• Target: 149
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (1)

• Sensitivity and specificity of the AI system in detecting gingival recession, compared to clinical probing measurements.

  -Primary Outcome 1 Outcome Measure: Sensitivity and specificity of the AI system for detecting gingival recession compared with clinical probing measurements. Primary Outcome 2 Outcome Measure: Agreement between the AI system and expert clinicians in classifying gingival recession according to the Cairo classification, assessed using Cohen's kappa coefficient.

  Through study completion, an average of 6 months

Secondary Outcomes (1)

• - Error in automated CEJ identification, compared to manual annotations.

  Immediately after AI analysis of the clinical images

Study Arms (1)

Gingival Recession Patients

This group consists of patients presenting with anterior gingival recession. Clinical intraoral images will be collected from eligible participants and used for the development and validation of an artificial intelligence-based classification model. The dataset includes cases with varying degrees and types of gingival recession according to established clinical classification criteria. No therapeutic intervention will be performed as part of the study, and all images will be analyzed for diagnostic and classification purposes only.

Diagnostic Test: Artificial Intelligence-Based Clinical Image Analysis Model

Interventions

Artificial Intelligence-Based Clinical Image Analysis Model DIAGNOSTIC_TEST

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.

Gingival Recession Patients

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population will consist of adult patients presenting with gingival recession affecting anterior teeth and attending the outpatient clinics of the Faculty of Dental Medicine for Girls, Al-Azhar University. Participants with clinically visible anterior gingival recession and adequate clinical photographs suitable for image analysis will be included in the study.

You may qualify if:

• Patients aged 18 years or older
• Presence of at least one anterior tooth exhibiting gingival recession classified according to the Cairo classification system (RT1, RT2, or RT3). - The gingival margin must be clearly visible.
• High-quality images (good focus, lighting, and resolution) are required.
• Clinically visible and intact cementoenamel junction (CEJ).

You may not qualify if:

• Presence of cervical restorations or fixed prostheses that interfere with CEJ identification.
• Patients undergoing active orthodontic treatment.
• Pregnant individuals, due to hormonal changes affecting gingival tissues.
• Images with poor photographic quality.

Sponsors & Collaborators

• Al-Azhar University: lead

Study Sites (1)

Faculty of Dental Medicine for Girls, Al-Azhar University

Cairo, Egypt

Location

MeSH Terms

Conditions

Gingival Recession

Condition Hierarchy (Ancestors)

Gingival Diseases; Periodontal Diseases; Mouth Diseases; Stomatognathic Diseases; Periodontal Atrophy

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Master's Degree Candidate

Study Record Dates

• First Submitted: June 30, 2026
• First Posted: July 9, 2026
• Study Start: June 15, 2025
• Primary Completion: January 15, 2026
• Study Completion: April 15, 2026
• Last Updated: July 9, 2026

Record last verified: 2026-07

Locations

EG (1)