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Comparison of Artificial Intelligence and Clinicians With Different Experience Levels in Assessing Gingival Phenotype

NCT07570290 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 40

Last updated 2026-05-11

No results posted yet for this study

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 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.

Conditions

  • Gingival Phenotype Assessment

Interventions

DIAGNOSTIC_TEST

Periodontal Probe Transparency Method

Standardized 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

Visual Assessment Method

Standardized intraoral photography of the maxillary anterior teeth without a periodontal probe, evaluated for gingival phenotype classification based on morphological features.

OTHER

Deep Learning-Based Artificial Intelligence Model

A deep learning image classification algorithm trained to assess probe visibility and gingival phenotype from standardized intraoral photographs.

Sponsors & Collaborators

  • Ondokuz Mayıs University

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-05-15
Primary Completion
2026-08-15
Completion
2026-10-15

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07570290 on ClinicalTrials.gov