Periodontal DiseasesNCT07689552
Deep Learning-Based Measurement of Keratinized Gingiva Width Using Smartphone-Acquired Clinical Images
An observational study of Periodontal Diseases, run by Al-Azhar University at 1 site in Egypt.
Trial summary
This study aims to develop and validate an artificial intelligence-based system for automated measurement of keratinized gingiva width using smartphone-acquired intraoral clinical photographs. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Periodontal Diseases
- What is being tested?
- Artificial Intelligence-Based Keratinized Gingiva Width Assessment
- How many people
- 50 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Jul 2025to 15 Mar 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 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Participants attending the clinic of the department of Periodontologly Faculty of Dental Medicine for girls Al-Azhar university who met the study eligibility criteria and provided smartphone-acquired intraoral clinical photographs for keratinized gingiva width assessment and artificial intelligence model validation.
Who may be able to join
- Patients aged 18 years or older.
- Patients with varying periodontal conditions thealthy. gingivitis, periodontitie.
- Patients willing to provide adormed consent.
Who cannot join
- Patients with a history of periodontal surgery within the past six montie
- Patients withsystemic conditions affecting oraltissue eg. diabetes.
- Very poor quality intra oral image.
Trial locations and contacts
1 site in 1 country.
Egypt1 site
Faculty of Dental Medicine for Girls, Al-Azhar University
Cairo, Cairo Governorate, 11754
About this study
Official title: A Deep Learning-Based Analytical Framework for Detection, Quantification, and Quality Assessment of Keratinized Gingival Tissues in Clinical Examination Images
This study aims to develop and validate an artificial intelligence-based system for automated measurement of keratinized gingiva width using smartphone-acquired intraoral clinical photographs. Standardized intraoral images will be collected and analyzed using a deep learning model, and the results will be compared with clinical measurements performed by calibrated expert examiners, which serve as the reference standard. The performance of the proposed system will be evaluated using accuracy metrics including Dice coefficient, Intersection over Union (IoU), precision, recall, and F1-score. This study seeks to support the integration of AI tools into periodontal diagnosis and clinical decision-making to improve measurement consistency and reduce inter-examiner variability.
Read the detailed description
This observational diagnostic validation study was conducted to develop and evaluate an artificial intelligence-based system for automated assessment of keratinized gingiva width (KGW) using smartphone-acquired intraoral clinical photographs.
Standardized intraoral images were collected from eligible participants following predefined inclusion and exclusion criteria. All images were captured using a smartphone under standardized clinical conditions to ensure uniformity in lighting, angulation, and image quality. Clinical measurements of keratinized gingiva width were independently performed by two calibrated expert examiners, serving as the reference (ground truth) standard.
A deep learning-based model was trained to segment and measure the keratinized gingival tissue from clinical images. The predicted measurements generated by the AI system were compared against the expert clinical measurements to evaluate model performance.
The performance of the system was assessed using multiple evaluation metrics, including accuracy, Dice similarity coefficient, Intersection over Union (IoU), precision, recall, and F1-score. Inter-examiner reliability between experts was also considered to ensure consistency of the reference standard.
The study aims to demonstrate the feasibility of integrating artificial intelligence into periodontal diagnostics, specifically for objective and reproducible measurement of keratinized gingiva width. The proposed system may contribute to reducing inter-operator variability and improving clinical efficiency in periodontal assessment.
- Conditions
- Periodontal Diseases
- Being tested
- Artificial Intelligence-Based Keratinized Gingiva Width Assessment
- Keywords
- Artificial Intelligence, Deep Learning, Keratinized Gingiva Width, KGW, Periodontology, Clinical Photography, Image Segmentation, Automated Measurement, Periodontal Diagnosis, Dental Artificial Intelligence, Computer Vision, Smartphone Imaging
- Medical subject headings
- Periodontal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-Based Keratinized Gingiva Width AssessmentDiagnostic test
Analysis of smartphone-acquired intraoral photographs using a deep learning model for automated measurement of keratinized gingiva width.
| Study group | Role | What they receive |
|---|---|---|
| Participants Undergoing Keratinized Gingiva Assessment | Participants whose smartphone-acquired intraoral clinical photographs were used for assessment of keratinized gingiva width. Clinical measurements performed by expert examiners served as the reference standard for validation of the artificial intelligence model. Receives: Diagnostic Test: Artificial Intelligence-Based Keratinized Gingiva Width Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of Artificial Intelligence-Based Keratinized Gingiva Width Measurement
Evaluation of the agreement between keratinized gingiva width measurements generated by the artificial intelligence model and reference measurements obtained by calibrated examiners using smartphone-acquired intraoral clinical photographs at the baseline clinical visit.
Measured: Baseline (single study visit)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jul 2026
- Study start1 Jul 2025
- Primary completion9 Jan 2026
- Study completion15 Mar 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Periodontal DiseasesStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 50 participants (actual)
Sponsor and investigators
- Lead sponsor
- Al-Azhar University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Salma Lamloum Mohamed Mohamed, Al-Azhar University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07689552
- Sponsor's protocol ID
- OMPDR 108-1q
- Status last verified
- Jul 2026
- First submitted
- 30 Jun 2026
- Last updated
- 8 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: IPD will not be shared to protect patient confidentiality and in compliance with institutional ethical guidelines. Data access is limited to the study investigators only.
Study at a glance
From the registry record, last updated by the sponsor on 8 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Deep Learning-Based Analytical Framework for Detection, Quantification, and Quality Assessment of Keratinized Gingival Tissues in Clinical Examination Images
- ClinicalTrials.gov ID
- NCT07689552
- Acronym / sponsor ID
- OMPDR 108-1q
- Conditions
- Periodontal Diseases
- Keywords
- Artificial Intelligence, Deep Learning, Keratinized Gingiva Width, KGW, Periodontology, Clinical Photography, Image Segmentation, Automated Measurement
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 50 participants (actual)
- Lead sponsor
- Al-Azhar UniversityUniversity, hospital or other
- Study start
- 1 Jul 2025 (actual)
- Primary completion
- 9 Jan 2026 (actual)
- Study completion
- 15 Mar 2026 (actual)
- First posted
- 8 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 validate an artificial intelligence-based system for automated measurement of keratinized gingiva width using smartphone-acquired intraoral clinical photographs. Standardized intraoral images will be collected and analyzed using a deep learning model, and the results will be compared with clinical measurements performed by calibrated expert examiners, which serve as the reference standard. The performance of the proposed system will be evaluated using accuracy metrics including Dice coefficient, Intersection over Union (IoU), precision, recall, and F1-score. This study seeks to support the integration of AI tools into periodontal diagnosis and clinical decision-making to improve measurement consistency and reduce inter-examiner variability.
Detailed description
This observational diagnostic validation study was conducted to develop and evaluate an artificial intelligence-based system for automated assessment of keratinized gingiva width (KGW) using smartphone-acquired intraoral clinical photographs.
Standardized intraoral images were collected from eligible participants following predefined inclusion and exclusion criteria. All images were captured using a smartphone under standardized clinical conditions to ensure uniformity in lighting, angulation, and image quality. Clinical measurements of keratinized gingiva width were independently performed by two calibrated expert examiners, serving as the reference (ground truth) standard.
A deep learning-based model was trained to segment and measure the keratinized gingival tissue from clinical images. The predicted measurements generated by the AI system were compared against the expert clinical measurements to evaluate model performance.
The performance of the system was assessed using multiple evaluation metrics, including accuracy, Dice similarity coefficient, Intersection over Union (IoU), precision, recall, and F1-score. Inter-examiner reliability between experts was also considered to ensure consistency of the reference standard.
The study aims to demonstrate the feasibility of integrating artificial intelligence into periodontal diagnostics, specifically for objective and reproducible measurement of keratinized gingiva width. The proposed system may contribute to reducing inter-operator variability and improving clinical efficiency in periodontal assessment.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Participants Undergoing Keratinized Gingiva Assessment | – | Diagnostic Test: Artificial Intelligence-Based Keratinized Gingiva Width AssessmentParticipants whose smartphone-acquired intraoral clinical photographs were used for assessment of keratinized gingiva width. Clinical measurements performed by expert examiners served as the reference standard for validation of the artificial intelligence model. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Participants attending the clinic of the department of Periodontologly Faculty of Dental Medicine for girls Al-Azhar university who met the study eligibility criteria and provided smartphone-acquired intraoral clinical photographs for keratinized gingiva width assessment and artificial intelligence model validation.
Key inclusion criteria (3 listed)
- Patients aged 18 years or older.
- Patients with varying periodontal conditions thealthy. gingivitis, periodontitie.
- Patients willing to provide adormed consent.
Key exclusion criteria (3 listed)
- Patients with a history of periodontal surgery within the past six montie
- Patients withsystemic conditions affecting oraltissue eg. diabetes.
- Very poor quality intra oral image.
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 Keratinized Gingiva Width AssessmentAnalysis of smartphone-acquired intraoral photographs using a deep learning model for automated measurement of keratinized gingiva width. | Diagnostic test | Participants Undergoing Keratinized Gingiva Assessment |
Primary outcome
Accuracy of Artificial Intelligence-Based Keratinized Gingiva Width Measurement
Time frame: Baseline (single study visit)
Evaluation of the agreement between keratinized gingiva width measurements generated by the artificial intelligence model and reference measurements obtained by calibrated examiners using smartphone-acquired intraoral clinical photographs at the baseline clinical visit.
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 8 Jul 2026.
- Individual participant dataSharing planned: No. IPD will not be shared to protect patient confidentiality and in compliance with institutional ethical guidelines. Data access is limited to the study investigators only.
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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 Periodontal Diseases, 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 3 inclusion and 3 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 8 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?
Accuracy of Artificial Intelligence-Based Keratinized Gingiva Width Measurement (time frame: Baseline (single study visit)). 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 8 Jul 2026; AzTrials last refreshed its copy on 26 Oct 2026.