Gingival InflammationNCT06830161
Artificial Intelligence (AI)-Powered Thermal Imaging for Gingival Inflammation Detection
An observational study of Gingival Inflammation and Machine Learning, run by Gazi University at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 25 yearsHealthy volunteers can take part
- Conditions the study is for
- Gingival Inflammation, Machine Learning, Thermal Imaging
- What is being tested?
- Gingival Thermal Images
- How many people
- 20 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 30 Jul 2024to 2 Jan 2025
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 25 years
- All sexes
- Healthy volunteers accepted
- Adult
Study population: The participants were selected from individuals presenting to the Department of Periodontology, Faculty of Dentistry, Gazi University who suffers from mouth breathing.
Who may be able to join
- Having at least 20 teeth
- Being between 18 and 25 years old and systemically healthy.
- No periodontal treatment within the last 6 months.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Gazi University Faculty of Dentistry Department of Periodontology
Ankara, Çankaya, 06510
About this study
Official title: AI-Assisted Thermal Imaging for Gingival Inflammation Assessment: A Novel Approach
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.
Read more
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.
Read the detailed description
Ethical Approval and Participant Selection This study was initiated following approval from the Ethics Committee of Gazi University (Meeting No: 13, dated 30.07.2024). Participants were selected from individuals presenting to the Department of Periodontology, Faculty of Dentistry, Gazi University.
Determination of Mouth Breathing
The diagnosis of mouth breathing was based on patients' medical history and clinical examination. Participants were asked about the use of oral breathing devices, whether they slept with their mouth open, and whether they experienced nocturnal awakening due to dry mouth. The following diagnostic tests were employed during the clinical examination to confirm mouth breathing:
Participants were instructed to close their lips and breathe through one nostril while the other nostril was occluded. Individuals with nasal breathing exhibited effective alar muscle function, which is typically absent in mouth breathers.
While participants were instructed to breathe normally, a mirror was held horizontally beneath the nostrils bilaterally. The presence of fog on the lower side of the mirror was considered an indicator of mouth breathing.
After evaluating the criteria for mouth breathing, individuals meeting the criteria were enrolled in the study based on the following inclusion and exclusion parameters:
Inclusion Criteria:
Presence of at least 20 teeth Age between 18 and 25 years, with systemic health No history of periodontal treatment within the last six months
Exclusion Criteria:
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 Presence of xerostomia or drug-induced gingival inflammation Current or former smoking history Acquisition of Thermal Images The thermal camera lens was fixed and focused on the gingival region being imaged throughout the recording process (Optris GmbH, PI 450, Berlin, Germany) [5].
After the initial thermographic image, participants were instructed to rinse with 20°C ice water for 60 seconds. Following the rinse, thermal images were captured at 1, 2, and 3 minutes.
Labeling Process A total of 82 thermal images from 20 participants were resized to 640 × 480 pixels and annotated using an online licensed browser tool (Make Sense AI). A total of 867 labeled data points were generated and divided into a training set (80% of the total) and a testing set (20% of the total) .
The labeling process involved tracing from the midpoint of the interdental papilla tip, following the juxta-gingival level to the midpoint of the adjacent interdental papilla, and extending vertically to the mucogingival junction. From this vertical endpoint, the boundary was traced back parallel to the starting point, returning to the projection of the initial papilla's midpoint. The boundaries were closed vertically at this projection .
Assessment of Inflammation
The degree of inflammation was evaluated using the Gingival Index . Images were categorized into four groups based on the highest inflammation grade observed in the region:
Inflammation 0: Normal gingiva Inflammation 1: Mild inflammation Inflammation 2: Moderate inflammation Inflammation 3: Severe inflammation
- Conditions
- Gingival Inflammation, Machine Learning, Thermal Imaging
- Being tested
- Gingival Thermal Images
- Medical subject headings
- Gingivitis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Gingival Thermal ImagesDiagnostic test
The degree of inflammation was evaluated using the Gingival Index
| Study group | Role | What they receive |
|---|---|---|
| Inflammation 0, Inflammation 1, Inflammation 2, Inflammation 3 | Normal Gingiva Mild Inflammation Moderate Inflammation Severe Inflammation Receives: Diagnostic Test: Gingival Thermal Images |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Feb 2025
- Study start30 Jul 2024
- Primary completion1 Jan 2025
- Study completion2 Jan 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gingival InflammationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 20 participants (actual)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Gazi University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Zeynep Turgut Çankaya, Gazi University
- Investigators
- Zeynep Turgut Çankaya, Principal investigator, Gazi University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06830161
- Sponsor's protocol ID
- 30.07.2024/13
- Status last verified
- Feb 2025
- First submitted
- 5 Feb 2025
- Last updated
- 17 Feb 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Upon request
Study at a glance
From the registry record, last updated by the sponsor on 17 Feb 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-Assisted Thermal Imaging for Gingival Inflammation Assessment: A Novel Approach
- ClinicalTrials.gov ID
- NCT06830161
- Acronym / sponsor ID
- 30.07.2024/13
- Conditions
- Gingival Inflammation, Machine Learning, Thermal Imaging
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 20 participants (actual)
- Lead sponsor
- Gazi UniversityUniversity, hospital or other
- Study start
- 30 Jul 2024 (actual)
- Primary completion
- 1 Jan 2025 (actual)
- Study completion
- 2 Jan 2025 (actual)
- First posted
- 17 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Ethical Approval and Participant Selection This study was initiated following approval from the Ethics Committee of Gazi University (Meeting No: 13, dated 30.07.2024). Participants were selected from individuals presenting to the Department of Periodontology, Faculty of Dentistry, Gazi University.
Determination of Mouth Breathing
The diagnosis of mouth breathing was based on patients' medical history and clinical examination. Participants were asked about the use of oral breathing devices, whether they slept with their mouth open, and whether they experienced nocturnal awakening due to dry mouth. The following diagnostic tests were employed during the clinical examination to confirm mouth breathing:
Participants were instructed to close their lips and breathe through one nostril while the other nostril was occluded. Individuals with nasal breathing exhibited effective alar muscle function, which is typically absent in mouth breathers.
While participants were instructed to breathe normally, a mirror was held horizontally beneath the nostrils bilaterally. The presence of fog on the lower side of the mirror was considered an indicator of mouth breathing.
After evaluating the criteria for mouth breathing, individuals meeting the criteria were enrolled in the study based on the following inclusion and exclusion parameters:
Inclusion Criteria:
Presence of at least 20 teeth Age between 18 and 25 years, with systemic health No history of periodontal treatment within the last six months
Exclusion Criteria:
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 Presence of xerostomia or drug-induced gingival inflammation Current or former smoking history Acquisition of Thermal Images The thermal camera lens was fixed and focused on the gingival region being imaged throughout the recording process (Optris GmbH, PI 450, Berlin, Germany) [5].
After the initial thermographic image, participants were instructed to rinse with 20°C ice water for 60 seconds. Following the rinse, thermal images were captured at 1, 2, and 3 minutes.
Labeling Process A total of 82 thermal images from 20 participants were resized to 640 × 480 pixels and annotated using an online licensed browser tool (Make Sense AI). A total of 867 labeled data points were generated and divided into a training set (80% of the total) and a testing set (20% of the total) .
The labeling process involved tracing from the midpoint of the interdental papilla tip, following the juxta-gingival level to the midpoint of the adjacent interdental papilla, and extending vertically to the mucogingival junction. From this vertical endpoint, the boundary was traced back parallel to the starting point, returning to the projection of the initial papilla's midpoint. The boundaries were closed vertically at this projection .
Assessment of Inflammation
The degree of inflammation was evaluated using the Gingival Index . Images were categorized into four groups based on the highest inflammation grade observed in the region:
Inflammation 0: Normal gingiva Inflammation 1: Mild inflammation Inflammation 2: Moderate inflammation Inflammation 3: Severe inflammation
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Target follow-up
- 6 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Inflammation 0, Inflammation 1, Inflammation 2, Inflammation 3 | – | Diagnostic Test: Gingival Thermal ImagesNormal Gingiva Mild Inflammation Moderate Inflammation Severe Inflammation |
Who may qualify
- Ages
- 18 to 25 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
- Study population
- The participants were selected from individuals presenting to the Department of Periodontology, Faculty of Dentistry, Gazi University who suffers from mouth breathing.
Key inclusion criteria (3 listed)
- Having at least 20 teeth
- Being between 18 and 25 years old and systemically healthy.
- No periodontal treatment within the last 6 months.
Key exclusion criteria (6 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Gingival Thermal ImagesThe degree of inflammation was evaluated using the Gingival Index | Diagnostic test | Inflammation 0, Inflammation 1, Inflammation 2, Inflammation 3 |
Primary outcome
Classification Performance of AI Model in Detecting Gingival Inflammation from Thermal Imaging Data
Time frame: 6 months
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.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Feb 2025.
- Individual participant dataSharing planned: Yes. Upon request
Related trials for Gingival Inflammation
Not yet recruitingPhase 2
Efficacy of Thymus Vulgaris Mouthwash in the Management of Biofilm-Induced Gingivitis
Plaque-Induced Gingivitis
Not yet recruitingObservational study
Circadian Clock Proteins in Gingival Crevicular Fluid of Individuals With and Without Circadian Rhythm Disruption
Gingivitis, Circadian Rhythm Disruption
Active, not recruitingObservational study
Comparison of Saliva Characteristics and Oral Health in Pregnant and Non-pregnant Women
Plaque Induced Gingivitis
Not yet recruiting
Effectiveness of Oil Pulling Using Oils in Comparison With Mouthwash for Gingivitis
Gingivitis
Not yet recruitingObservational study
Salivary and GCF Biomarkers in Periodontal Health, Gingivitis and Periodontitis
Periodontal Health, Gingivitis
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Zeynep Turgut Çankaya principal investigator
Gazi University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gingival Inflammation and Machine Learning, run by Gazi University at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 17 Feb 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Classification Performance of AI Model in Detecting Gingival Inflammation from Thermal Imaging Data (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Feb 2025.