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

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.

Study Overview

• Status: Completed
• Conditions: Periodontal Diseases
• Intervention / Treatment: Diagnostic test: Artificial Intelligence-Based Keratinized Gingiva Width Assessment

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 Type: Observational
• Enrollment (Actual): 50

Contacts and Locations

Study Locations

• Egypt
  • Cairo Governorate
    • Cairo, Cairo Governorate, Egypt, 11754
      • Faculty of Dental Medicine for Girls, Al-Azhar University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

Inclusion Criteria:

• Patients aged 18 years or older.

Patients with varying periodontal conditions thealthy. gingivitis, periodontitie.

Patients willing to provide adormed consent.

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
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; Analysis of smartphone-acquired intraoral photographs using a deep learning model for automated measurement of keratinized gingiva width.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)

Collaborators and Investigators

• Sponsor: Al-Azhar University

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2025
• Primary Completion (Actual): January 9, 2026
• Study Completion (Actual): March 15, 2026

Study Registration Dates

• First Submitted: June 30, 2026
• First Submitted That Met QC Criteria: July 7, 2026
• First Posted (Actual): July 8, 2026

Study Record Updates

• Last Update Posted (Actual): July 8, 2026
• Last Update Submitted That Met QC Criteria: July 7, 2026
• Last Verified: July 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Deep Learning; Computer Vision; Periodontology; Automated Measurement; Keratinized Gingiva Width; KGW; Clinical Photography; Image Segmentation; Periodontal Diagnosis; Dental Artificial Intelligence; Smartphone Imaging
• Additional Relevant MeSH Terms: Mouth Diseases; Stomatognathic Diseases; Periodontal Diseases
• Other Study ID Numbers: OMPDR 108-1q

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: 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.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No