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

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.

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Key information

Conditions

Gingival Recessions Gingival Diseases Gingival Recession Mouth Diseases Periodontal Atrophy Periodontal Diseases Stomatognathic Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Faculty of Dental Medicine for Girls, Al-Azhar University

Cairo, Egypt

About this study

This study is designed to develop and validate an artificial intelligence (AI)-based clinical image analysis model for the detection, classification, and management recommendation of anterior gingival recession. Gingival recession is a common periodontal condition characterized by apical displacement of the gingival margin, which may lead to aesthetic concerns, dentinal hypersensitivity, and increased risk of root caries.

Clinical intraoral images of patients presenting with anterior gingival recession will be collected following standardized imaging protocols. The dataset will be used to train, validate, and test a machine learning model capable of identifying the presence of gingival recession and classifying its severity and/or type according to established periodontal classification systems.

The AI model will also be designed to generate preliminary management recommendations based on the detected class, supporting clinical decision-making. Model performance will be evaluated using standard metrics such as accuracy, sensitivity, specificity, precision, recall, and area under the receiver operating characteristic curve (AUC-ROC).

The study is observational in nature with a diagnostic and model-development component. All patient data will be anonymized to ensure confidentiality, and ethical approval will be obtained prior to data collection. The final output is intended to support clinicians in improving diagnostic consistency and treatment planning efficiency for anterior gingival recession.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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).

Exclusion criteria

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.

Treatment and study plan

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.

Primary outcomes

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

Time frame: Through study completion, an average of 6 months

-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.

Secondary outcomes

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

Time frame: Immediately after AI analysis of the clinical images
- Error in automated CEJ identification, compared to manual annotations.
- Concordance rate between AI-generated treatment recommendations and those proposed by experienced periodontists.

Sponsors and collaborators

Lead sponsor

Al-Azhar University

Other

Registry information

Important dates

Study start: 2025
Primary completion: 2026
Study completion: 2026
First posted: Jul 9, 2026
Registry last updated: Jul 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed