Gingival Phenotype AssessmentNCT07570290
Comparison of Artificial Intelligence and Clinicians With Different Experience Levels in Assessing Gingival Phenotype
An observational study of Gingival Phenotype Assessment, run by Ondokuz Mayıs University.
Trial summary
The goal of this observational study is to compare the performance of clinicians with different experience levels and a deep learning-based artificial intelligence (AI) model in assessing gingival phenotype using two diagnostic methods: the periodontal probe transparency method and visual assessment from standardized… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Gingival Phenotype Assessment
- What is being tested?
- Periodontal Probe Transparency Method, Visual Assessment Method and Deep Learning-Based Artificial Intelligence Model
- How many people
- 40 participants
- Where
- No sites listed yet
- Study dates
- 15 May 2026to 15 Oct 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population will consist of systemically and periodontally healthy adults attending the Department of Periodontology at Ondokuz Mayıs University, Faculty of Dentistry, for routine dental care or check-up. Eligible participants will have natural maxillary anterior incisors and meet all inclusion criteria. Additionally, the examiner population will include: Periodontology research assistants currently working in the department. Fourth- and fifth-year dental intern students who have completed the periodontology clinical rotation.
Who may be able to join
- Research assistants: Must be currently working in the Department of Periodontology.
- Dental Intern Students: Fourth- or fifth-year students who have completed periodontology clinical rotation.
Who cannot join
- Presence of fixed crowns or cervical restorations on the evaluated teeth.
- Pregnant or breastfeeding women.
- Signs of gingival inflammation or periodontal disease with attachment loss.
- Presence of buccal gingival recession.
- Use of medications known to cause gingival enlargement.
- Presence of congenital anomalies or dental structural defects.
- Those who are confirmed to be color blind by the Ishihara test
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
The goal of this observational study is to compare the performance of clinicians with different experience levels and a deep learning-based artificial intelligence (AI) model in assessing gingival phenotype using two diagnostic methods: the periodontal probe transparency method and visual assessment from standardized clinical photographs. The main questions the study aims to answer are:
Can AI achieve comparable accuracy to human examiners in both probe transparency and visual assessment methods?
Read more
Does examiner experience level influence diagnostic performance and agreement with the reference standard in these methods?
Researchers will compare AI, dental students, and periodontology research assistants to determine accuracy, sensitivity, specificity, and agreement with the gold standard for each method.
Participants will:
Undergo standardized intraoral photography of maxillary anterior teeth, with and without a periodontal probe in place, following a validated protocol.
Have gingival phenotype determined by a reference periodontologist using the probe transparency method as the gold standard.
Have their photographs evaluated by AI, dental students, and research assistants for phenotype classification using both methods.
Read the detailed description
Gingival phenotype, representing the thickness and morphological characteristics of the gingival soft tissues, plays a critical role in periodontal health, treatment planning, and the long-term stability of clinical outcomes. A thin phenotype is associated with increased risk of gingival recession, papilla loss, and inflammatory complications, while a thick phenotype offers better soft tissue stability but may mask inflammation. Accurate and reproducible assessment of gingival phenotype is therefore essential in clinical dentistry.
The periodontal probe transparency method is considered the gold standard for phenotype assessment due to its simplicity and non-invasiveness. In this method, a periodontal probe is inserted into the sulcus from the buccal aspect, and if the probe is visible through the gingival tissue, the phenotype is classified as thin; if not visible, it is classified as thick. However, the method is susceptible to variability depending on examiner experience, lighting conditions, and subjective interpretation.
Visual assessment, which relies solely on the inspection of gingival and tooth morphology in photographs without a probe, offers a non-contact alternative but is similarly subject to examiner-related variability. These limitations highlight the need for standardized and objective approaches to phenotype determination.
Artificial intelligence (AI), particularly deep learning-based image analysis, has shown promising results in dental diagnostics, enabling automated classification of clinical images with high accuracy and reproducibility. In periodontal research, AI has been applied for lesion detection and radiographic interpretation, but its application in gingival phenotype assessment-especially using the probe transparency method and visual assessment-remains unexplored.
This observational study aims to compare the diagnostic performance of a deep learning-based AI model with human examiners of different experience levels (periodontology residents vs. dental students) in assessing gingival phenotype from standardized intraoral photographs using both the periodontal probe transparency method and visual assessment. The reference standard will be the classification provided by an experienced periodontologist using the probe transparency method in a clinical setting.
The study will evaluate and compare accuracy, sensitivity, specificity, and inter-/intra-examiner agreement across examiner groups and the AI model. The findings are expected to provide insights into the potential of AI as a standardizing tool, reducing inter-examiner variability and supporting clinical decision-making, particularly for less experienced clinicians. Additionally, the study may inform the integration of AI-assisted diagnostic tools in dental education and practice, improving training efficiency and clinical outcomes.
- Conditions
- Gingival Phenotype Assessment
- Being tested
- Periodontal Probe Transparency Method, Visual Assessment Method, Deep Learning-Based Artificial Intelligence Model
- Keywords
- gingiva, Gingival phenotype, Deep Learning, Transparency Method
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Periodontal Probe Transparency MethodDiagnostic test
Standardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status.
Visual Assessment MethodDiagnostic test
Standardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.
Deep Learning-Based Artificial Intelligence ModelOther
A deep learning image classification algorithm trained to assess probe visibility and gingival phenotype from standardized intraoral photographs.
| Study group | Role | What they receive |
|---|---|---|
| Periodontology Research Assistants | Research assistants in periodontology will assess standardized intraoral photographs using both the periodontal probe transparency method and visual assessment to classify gingival phenotype. Receives: Diagnostic Test: Periodontal Probe Transparency Method; Diagnostic Test: Visual Assessment Method | |
| Dental Students | Fourth- and fifth-year dental intern students will assess standardized intraoral photographs using both the periodontal probe transparency method and visual assessment to classify gingival phenotype. Receives: Diagnostic Test: Periodontal Probe Transparency Method; Diagnostic Test: Visual Assessment Method | |
| Artificial Intelligence Model | A deep learning-based image classification model will analyze standardized intraoral photographs, detecting probe visibility and classifying gingival phenotype according to the periodontal probe transparency method and visual assessment criteria. Receives: Diagnostic Test: Periodontal Probe Transparency Method; Diagnostic Test: Visual Assessment Method; Other: Deep Learning-Based Artificial Intelligence Model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of Each Examiner Group and AI Model in the Periodontal Probe Transparency Method
Accuracy in determining probe visibility (visible vs. not visible) compared to the gold standard classification by an experienced periodontologist.
Measure Type: Proportion (%). Analysis: Accuracy, sensitivity, specificity, and Cohen's kappa coefficient will be calculated.
Measured: At the time of image evaluation (single session).
Secondary outcomes (3)
Diagnostic Accuracy of Each Examiner Group and AI Model in Visual Assessment Method
Accuracy in classifying gingival phenotype (thin vs. thick) without probe, compared to the gold standard classification.
Measure Type: Proportion (%).
Measured: At the time of image evaluation (single session).
Agreement Between Examiner Groups and AI Model
Inter-examiner and intra-examiner agreement for each method, evaluated using Cohen's kappa coefficient and intraclass correlation coefficient (ICC).
Measured: At the time of image evaluation and at 2-week retest (for a random subset of evaluators).
Effect of Examiner Experience Level on Diagnostic Performance
Comparison of accuracy and agreement between research assistants and dental intern students for each method.
Proportion (%), agreement statistic.
Measured: At the time of image evaluation (single session).
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 May 2026
- Study start15 May 2026 (estimated)
- Primary completion15 Aug 2026 (estimated)
- Study completion15 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gingival Phenotype AssessmentStudies of the same condition, open and under way.
- More trials run by Ondokuz Mayıs UniversityThe sponsor's other registered studies.
- 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
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ondokuz Mayıs University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sude Yildirim, Ondokuz Mayıs University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07570290
- Sponsor's protocol ID
- OMUKAEK NO:225/335
- Status last verified
- May 2026
- First submitted
- 29 Apr 2026
- Last updated
- 11 May 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified individual participant data (IPD), including demographic characteristics, periodontal measurements, and standardized intraoral photographs, may be shared upon reasonable request for academic purposes. Access will require a data use agreement and approval by the principal investigator.
Study at a glance
From the registry record, last updated by the sponsor on 11 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of Artificial Intelligence and Clinicians With Different Experience Levels in Assessing Gingival Phenotype
- ClinicalTrials.gov ID
- NCT07570290
- Acronym / sponsor ID
- OMUKAEK NO:225/335
- Conditions
- Gingival Phenotype Assessment
- Keywords
- gingiva, Gingival phenotype, Deep Learning, Transparency Method
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Ondokuz Mayıs UniversityUniversity, hospital or other
- Study start
- 15 May 2026 (estimated)
- Primary completion
- 15 Aug 2026 (estimated)
- Study completion
- 15 Oct 2026 (estimated)
- First posted
- 6 May 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.
The goal of this observational study is to compare the performance of clinicians with different experience levels and a deep learning-based artificial intelligence (AI) model in assessing gingival phenotype using two diagnostic methods: the periodontal probe transparency method and visual assessment from standardized clinical photographs. The main questions the study aims to answer are:
Can AI achieve comparable accuracy to human examiners in both probe transparency and visual assessment methods?
Does examiner experience level influence diagnostic performance and agreement with the reference standard in these methods?
Researchers will compare AI, dental students, and periodontology research assistants to determine accuracy, sensitivity, specificity, and agreement with the gold standard for each method.
Participants will:
Undergo standardized intraoral photography of maxillary anterior teeth, with and without a periodontal probe in place, following a validated protocol.
Have gingival phenotype determined by a reference periodontologist using the probe transparency method as the gold standard.
Have their photographs evaluated by AI, dental students, and research assistants for phenotype classification using both methods.
Detailed description
Gingival phenotype, representing the thickness and morphological characteristics of the gingival soft tissues, plays a critical role in periodontal health, treatment planning, and the long-term stability of clinical outcomes. A thin phenotype is associated with increased risk of gingival recession, papilla loss, and inflammatory complications, while a thick phenotype offers better soft tissue stability but may mask inflammation. Accurate and reproducible assessment of gingival phenotype is therefore essential in clinical dentistry.
The periodontal probe transparency method is considered the gold standard for phenotype assessment due to its simplicity and non-invasiveness. In this method, a periodontal probe is inserted into the sulcus from the buccal aspect, and if the probe is visible through the gingival tissue, the phenotype is classified as thin; if not visible, it is classified as thick. However, the method is susceptible to variability depending on examiner experience, lighting conditions, and subjective interpretation.
Visual assessment, which relies solely on the inspection of gingival and tooth morphology in photographs without a probe, offers a non-contact alternative but is similarly subject to examiner-related variability. These limitations highlight the need for standardized and objective approaches to phenotype determination.
Artificial intelligence (AI), particularly deep learning-based image analysis, has shown promising results in dental diagnostics, enabling automated classification of clinical images with high accuracy and reproducibility. In periodontal research, AI has been applied for lesion detection and radiographic interpretation, but its application in gingival phenotype assessment-especially using the probe transparency method and visual assessment-remains unexplored.
This observational study aims to compare the diagnostic performance of a deep learning-based AI model with human examiners of different experience levels (periodontology residents vs. dental students) in assessing gingival phenotype from standardized intraoral photographs using both the periodontal probe transparency method and visual assessment. The reference standard will be the classification provided by an experienced periodontologist using the probe transparency method in a clinical setting.
The study will evaluate and compare accuracy, sensitivity, specificity, and inter-/intra-examiner agreement across examiner groups and the AI model. The findings are expected to provide insights into the potential of AI as a standardizing tool, reducing inter-examiner variability and supporting clinical decision-making, particularly for less experienced clinicians. Additionally, the study may inform the integration of AI-assisted diagnostic tools in dental education and practice, improving training efficiency and clinical outcomes.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Periodontology Research Assistants | – | Diagnostic Test: Periodontal Probe Transparency Method, Diagnostic Test: Visual Assessment MethodResearch assistants in periodontology will assess standardized intraoral photographs using both the periodontal probe transparency method and visual assessment to classify gingival phenotype. |
| Dental Students | – | Diagnostic Test: Periodontal Probe Transparency Method, Diagnostic Test: Visual Assessment MethodFourth- and fifth-year dental intern students will assess standardized intraoral photographs using both the periodontal probe transparency method and visual assessment to classify gingival phenotype. |
| Artificial Intelligence Model | – | Diagnostic Test: Periodontal Probe Transparency Method, Diagnostic Test: Visual Assessment Method, Other: Deep Learning-Based Artificial Intelligence ModelA deep learning-based image classification model will analyze standardized intraoral photographs, detecting probe visibility and classifying gingival phenotype according to the periodontal probe transparency method and visual assessment criteria. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of systemically and periodontally healthy adults attending the Department of Periodontology at Ondokuz Mayıs University, Faculty of Dentistry, for routine dental care or check-up. Eligible participants will have natural maxillary anterior incisors and meet all inclusion criteria. Additionally, the examiner population will include: Periodontology research assistants currently working in the department. Fourth- and fifth-year dental intern students who have completed the periodontology clinical rotation.
Key inclusion criteria (2 listed)
- Research assistants: Must be currently working in the Department of Periodontology.
- Dental Intern Students: Fourth- or fifth-year students who have completed periodontology clinical rotation.
Key exclusion criteria (7 listed)
- Presence of fixed crowns or cervical restorations on the evaluated teeth.
- Pregnant or breastfeeding women.
- Signs of gingival inflammation or periodontal disease with attachment loss.
- Presence of buccal gingival recession.
- Use of medications known to cause gingival enlargement.
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 |
|---|---|---|
| Periodontal Probe Transparency MethodStandardized intraoral photography of the maxillary anterior teeth with a periodontal probe placed according to the transparency method protocol to determine probe visibility status. | Diagnostic test | Artificial Intelligence Model, Dental Students, Periodontology Research Assistants |
| Visual Assessment MethodStandardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features. | Diagnostic test | Artificial Intelligence Model, Dental Students, Periodontology Research Assistants |
| Deep Learning-Based Artificial Intelligence ModelA deep learning image classification algorithm trained to assess probe visibility and gingival phenotype from standardized intraoral photographs. | Other | Artificial Intelligence Model |
Primary outcome
Diagnostic Accuracy of Each Examiner Group and AI Model in the Periodontal Probe Transparency Method
Time frame: At the time of image evaluation (single session).
Accuracy in determining probe visibility (visible vs. not visible) compared to the gold standard classification by an experienced periodontologist. Measure Type: Proportion (%). Analysis: Accuracy, sensitivity, specificity, and Cohen's kappa coefficient will be calculated.
3 secondary outcomes
Diagnostic Accuracy of Each Examiner Group and AI Model in Visual Assessment Method
Time frame: At the time of image evaluation (single session).
Agreement Between Examiner Groups and AI Model
Time frame: At the time of image evaluation and at 2-week retest (for a random subset of evaluators).
Effect of Examiner Experience Level on Diagnostic Performance
Time frame: At the time of image evaluation (single session).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 May 2026.
- Ondokuz Mayis University Clinical Research Ethics CommitteeLink provided by the sponsor.
- Individual participant dataSharing planned: Yes. De-identified individual participant data (IPD), including demographic characteristics, periodontal measurements, and standardized intraoral photographs, may be shared upon reasonable request for academic purposes. Access will require a data use agreement and approval by the principal investigator.
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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.
Questions about this trial
What is this clinical trial studying?
An observational study of Gingival Phenotype Assessment, run by Ondokuz Mayıs University.
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 2 inclusion and 7 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: opening soon, as of the sponsor's last update on 11 May 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sude Yıldırım Bolat, DDS (+905378947645, sugde.sude@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of Each Examiner Group and AI Model in the Periodontal Probe Transparency Method (time frame: At the time of image evaluation (single session).). 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 11 May 2026.