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

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Key information

Conditions

Periodontal Diseases Mouth Diseases Stomatognathic Diseases

Age range

18 year–65 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Faculty of Dental Medicine for Girls, Al-Azhar University

Cairo, Cairo Governorate, 11754, Egypt

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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.

Primary outcomes

Accuracy of Artificial Intelligence-Based Keratinized Gingiva Width Measurement

Time frame: Baseline (single study visit)

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.

Sponsors and collaborators

Lead sponsor

Al-Azhar University

Other

Registry information

Official study title

A Deep Learning-Based Analytical Framework for Detection, Quantification, and Quality Assessment of Keratinized Gingival Tissues in Clinical Examination Images

Important dates

Study start: 2025
Primary completion: 2026
Study completion: 2026
First posted: Jul 8, 2026
Registry last updated: Jul 8, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed