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

NCT07689552 | Completed

• 1 other identifier: OMPDR 108-1q
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 1

Brief 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

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

Outcome Measures

Primary Outcomes (1)

• 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.

  Baseline (single study visit)

Study Arms (1)

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.

Diagnostic Test: Artificial Intelligence-Based Keratinized Gingiva Width Assessment

Interventions

Artificial Intelligence-Based Keratinized Gingiva Width Assessment DIAGNOSTIC_TEST

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

Participants Undergoing Keratinized Gingiva Assessment

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• Patients aged 18 years or older.
• Patients with varying periodontal conditions thealthy. gingivitis, periodontitie.
• Patients willing to provide adormed consent.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Al-Azhar University: lead

Study Sites (1)

Faculty of Dental Medicine for Girls, Al-Azhar University

Cairo, Cairo Governorate, 11754, Egypt

Location

MeSH Terms

Conditions

Periodontal Diseases

Condition Hierarchy (Ancestors)

Mouth Diseases; Stomatognathic Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Master's Degree Candidate, Faculty of Dental Medicine for Girls, Al-Azhar University

Study Record Dates

• First Submitted: June 30, 2026
• First Posted: July 8, 2026
• Study Start: July 1, 2025
• Primary Completion: January 9, 2026
• Study Completion: March 15, 2026
• Last Updated: July 8, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

EG (1)