Gingival RecessionsNCT07693322
Development of an Artificial Intelligence-Based Clinical Image Model for Detection, Classification, and Management Recommendations of Anterior Gingival Recession
An observational study of Gingival Recessions, run by Al-Azhar University at 1 site in Egypt.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gingival Recessions
- What is being tested?
- Artificial Intelligence-Based Clinical Image Analysis Model
- How many people
- 149 participants
- Where
- 1 siteEgypt
- Study dates
- 15 Jun 2025to 15 Apr 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the detailed description
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.
- Conditions
- Gingival Recessions
- Being tested
- Artificial Intelligence-Based Clinical Image Analysis Model
- Medical subject headings
- Gingival Recession
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-Based Clinical Image Analysis ModelDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Artificial Intelligence-Based Clinical Image Analysis Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Through study completion, an average of 6 months
Secondary outcomes (1)
- Error in automated CEJ identification, compared to manual annotations.
- Error in automated CEJ identification, compared to manual annotations.
- Concordance rate between AI-generated treatment recommendations and those proposed by experienced periodontists.
Measured: Immediately after AI analysis of the clinical images
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jul 2026
- Study start15 Jun 2025
- Primary completion15 Jan 2026
- Study completion15 Apr 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gingival RecessionsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 149 participants (actual)
Sponsor and investigators
- Lead sponsor
- Al-Azhar University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Abeer Rashed Murshed, Al-Azhar University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07693322
- Sponsor's protocol ID
- OMPDR-108-1r
- Status last verified
- Jul 2026
- First submitted
- 30 Jun 2026
- Last updated
- 9 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an Artificial Intelligence-Based Clinical Image Model for Detection, Classification, and Management Recommendations of Anterior Gingival Recession
- ClinicalTrials.gov ID
- NCT07693322
- Acronym / sponsor ID
- OMPDR-108-1r
- Conditions
- Gingival Recessions
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 149 participants (actual)
- Lead sponsor
- Al-Azhar UniversityUniversity, hospital or other
- Study start
- 15 Jun 2025 (actual)
- Primary completion
- 15 Jan 2026 (actual)
- Study completion
- 15 Apr 2026 (actual)
- First posted
- 9 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
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.
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Gingival Recession Patients | – | Diagnostic Test: Artificial Intelligence-Based Clinical Image Analysis ModelThis 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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).
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence-Based Clinical Image Analysis ModelAn 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. | Diagnostic test | Gingival Recession Patients |
Primary outcome
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.
1 secondary outcome
- Error in automated CEJ identification, compared to manual annotations.
Time frame: Immediately after AI analysis of the clinical images
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jul 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gingival Recessions, run by Al-Azhar University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 9 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jul 2026.