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Deep Learning-Based Measurement of Keratinized Gingiva Width Using Smartphone-Acquired Clinical Images

NCT07689552 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 50

Last updated 2026-07-08

No results posted yet for this study

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. 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.

Conditions

  • Periodontal Diseases

Interventions

DIAGNOSTIC_TEST

Artificial Intelligence-Based Keratinized Gingiva Width Assessment

Analysis of smartphone-acquired intraoral photographs using a deep learning model for automated measurement of keratinized gingiva width.

Sponsors & Collaborators

  • Al-Azhar University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-07-01
Primary Completion
2026-01-09
Completion
2026-03-15

Countries

  • Egypt

Study Locations

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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 NCT07689552 on ClinicalTrials.gov