Artificial Intelligence (AI)-Powered Thermal Imaging for Gingival Inflammation Detection

NCT06830161 | Completed DMC

• 1 other identifier: 30.07.2024/13
• Study Type: observational
• Target: 20
• Locations: 1 country
• Sites: 1

Brief Summary

This study investigates a novel approach for detecting gingival inflammation using thermal imaging and artificial intelligence (AI). Thermal imaging is a technique that utilizes heat to generate detailed images, while AI assists in analyzing these images to identify patterns. Unlike traditional methods that require direct contact or visual examination, this approach is non-invasive, eliminating the need for physical interaction with the gingiva or reliance on subjective assessments. A key aspect of this study is its focus on individuals with mouth breathing, a condition that complicates gingival health monitoring. By utilizing thermal imaging, the study successfully detected and classified gingival inflammation levels (healthy, mild, moderate, or severe) based on heat distribution patterns. Additionally, specific temperature thresholds were established to differentiate between healthy and inflamed gingival tissues in this patient group, representing a novel contribution to the field. The developed AI system demonstrated high accuracy in identifying inflammation. This technology has the potential to facilitate earlier detection of gingival disease, even before clinical symptoms become evident. Furthermore, it offers a fast, painless, and reliable method for monitoring gingival health over time, enhancing accessibility and improving patient experience in dental care. These findings suggest that the integration of thermal imaging and AI could significantly improve the diagnosis and management of gingival diseases. Future research could further refine this technology by expanding the sample size and optimizing analytical models to enhance accuracy and widespread applicability.

Conditions

Gingival Inflammation; Machine Learning; Thermal Imaging

Outcome Measures

Primary Outcomes (1)

• Classification Performance of AI Model in Detecting Gingival Inflammation from Thermal Imaging Data

  The performance of the AI model in detecting gingival inflammation is assessed using classification metrics derived from thermal imaging data. The model's effectiveness is evaluated based on overall accuracy, precision, sensitivity (recall), specificity, and F1-score. The classification process is performed using the XGBoost algorithm, with a 5-fold cross-validation approach to ensure reliability. The final accuracy and performance metrics are calculated as the mean and standard deviation of cross-validation results.

  6 months

Study Arms (1)

Inflammation 0, Inflammation 1, Inflammation 2, Inflammation 3

Normal Gingiva Mild Inflammation Moderate Inflammation Severe Inflammation

Diagnostic Test: Gingival Thermal Images

Interventions

Gingival Thermal Images DIAGNOSTIC_TEST

The degree of inflammation was evaluated using the Gingival Index

Inflammation 0, Inflammation 1, Inflammation 2, Inflammation 3

Eligibility Criteria

• Age: 18 Years - 25 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

The participants were selected from individuals presenting to the Department of Periodontology, Faculty of Dentistry, Gazi University who suffers from mouth breathing.

You may qualify if:

• Having at least 20 teeth
• Being between 18 and 25 years old and systemically healthy.
• No periodontal treatment within the last 6 months.

You may not qualify if:

• Presence of any acute infection
• Use of systemic antibiotics or anti-inflammatory drugs within the past three months
• History of systemic diseases
• Pregnancy and/or lactation
• Xerostomia or drug-induced gingival inflammation
• Current or former smokers

Sponsors & Collaborators

• Gazi University: lead

Study Sites (1)

Gazi University Faculty of Dentistry Department of Periodontology

Ankara, Çankaya, 06510, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Gingivitis

Condition Hierarchy (Ancestors)

Infections; Gingival Diseases; Periodontal Diseases; Mouth Diseases; Stomatognathic Diseases

Study Officials

• Zeynep Turgut Çankaya

  Gazi University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Target Duration: 6 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Gazi University

Study Record Dates

• First Submitted: February 5, 2025
• First Posted: February 17, 2025
• Study Start: July 30, 2024
• Primary Completion: January 1, 2025
• Study Completion: January 2, 2025
• Last Updated: February 17, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• Time Frame: always

Locations

TU (1)