Dental Plaque (Diagnosis)NCT06603233

Microbial Dental Plaque Analysis in Young Permanent Teeth Using Deep Learning

A study testing The Difference Between The AI Model and Dentists Group and Deep Learning Models in people with Dental Plaque (Diagnosis) and Deep Learning, run by Ankara Medipol University at 1 site in Turkey (Türkiye).

Completed

Finished. The study has ended and is not enrolling. Results may be posted on the official record.

Official record

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Trial summary

Who is it for?: Ages 8 to 13 yearsHealthy volunteers can take part
Conditions the study is for: Dental Plaque (Diagnosis), Deep Learning
What is being tested?: The Difference Between The AI Model and Dentists Group and Deep Learning Models
Could I receive a placebo?: No, everyone receives an active treatment
How many people: 31 participants
Where: 1 siteTurkey (Türkiye)
Study dates: 1 Jun 2023to 1 Nov 2023
Phase: Interventional
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 8 to 13 years
All sexes
Healthy volunteers accepted
Child

Who may be able to join

Anterior young permanent teeth

Who cannot join

Anterior young permanent teeth exhibiting disruptions in enamel tissue integrity such as decay
Hypoplasia, hypomineralization
Restored and prosthetically treated teeth
Young permanent teeth located in the posterior region
Primary teeth

Trial locations and contacts

1 site in 1 country.

Turkey (Türkiye)1 site

Banu Çiçek Tez
Istanbul, Üsküdar

Other trials in Turkey (Türkiye)

About this study

Official title: Microbial Dental Plaque Analysis in Young Permanent Teeth Using Deep Learning in Children Aged 8-13 Years

Background: Dental plaque contributes to a number of common oral conditions such as caries, gingivitis, and periodontitis. As a result, detection and management of plaque is of great importance for the oral health of individuals. The primary objectives of this study were to design a deep learning model for the detection and segmentation of plaque in young permanent teeth and to evaluate the diagnostic accuracy of the model. Methods: The dataset contains 506 dental images from 31 patients aged 8 to 13 years. Six state-of-the-art models were trained and tested using this dataset. The U-Net Transformer model was compared with three dentists for clinical applicability using 35 randomly selected images from the test set.

Read the detailed description

Dental plaque is defined as a microbial community embedded in a matrix composed of polymers derived from bacteria and the content of saliva that develops on the surface of the teeth. Microbial dental plaque is adsorbed onto the tooth surface within seconds after dental cleaning and persists functionally. These molecules primarily exist in the fluid of the subgingival sulcus, along with saliva, and demonstrate settlement in this area. The primary etiological factor for gingivitis and periodontitis is bacterial plaque, which can lead to the destruction of gingival tissues and periodontal attachment. In children, if oral hygiene is not established immediately after tooth eruption, and regular brushing habits are not instilled, the bacterial biofilm layer can settle on the tooth surfaces and gingival margins associated with the oral environment, initiating gingival inflammation.

The early detection and treatment of periodontal diseases at the initial stages in children are clinically important, as these conditions can intensify and lead to adverse outcomes in later periods. Bacterial plaque is the primary etiological factor for gingival diseases in children. Identifying and distinguishing microbial dental plaque by patients can be challenging. Plaques can be detected through routine clinical practice using periodontal probes and/or plaque-disclosing solutions. Although these methods are widely employed, they may yield subjective results. However, these assessment methods can be cumbersome, time-consuming, and unsuccessful in noncooperative children. Additionally, plaque-disclosing solutions used for microbial dental plaque detection may temporarily stain the oral mucosa and lips. The literature also includes digital imaging analyses such as laser-induced autofluorescence spectroscopy and HIS color space for the detection of microbial dental plaque. However, the drawbacks, such as the high cost of equipment and technical standardization, limit their use .

For these reasons, this study aims to develop an affordable and easily accessible artificial intelligence (AI) model for the early and accurate diagnosis of microbial dental plaque in children. The aim is to prevent various periodontal problems and provide motivation for oral hygiene by evaluating the diagnostic and detection performance of this AI model.

With advancements in artificial intelligence for image processing, research on detecting, segmenting, and quantifying dental plaque in images captured by dental cameras has significantly increased. One study attempted to detect dental plaque using an Enhanced K-Means machine learning algorithm. Additionally, a Mask R-CNN-based dental health Internet of Things (IoT) platform was developed to classify seven different oral diseases, including dental plaque, with a perfect accuracy rate for plaque recognition, although not for segmentation.

While the U-Net model is widely regarded as successful and mainstream in the domain of biomedical image processing, there are no studies in the literature on the analysis of dental plaque with U-Net and its variants. Additionally, no studies have been encountered regarding the analysis of dental plaque in young permanent teeth of children. Hence, this study endeavors to train six state-of-the-art artificial intelligence models, incorporating variations of the U-Net model, for the purpose of dental plaque prediction in young permanent teeth of children. Subsequently, their performances are meticulously summarized and presented for comprehensive analysis. Finally, to validate the clinical feasibility of the best performing model, statistical hypothesis tests are performed that compares the predictions of the AI model with the assessments from three dentists.

Conditions: Dental Plaque (Diagnosis), Deep Learning
Being tested: The Difference Between The AI Model and Dentists Group, Deep Learning Models
Keywords: Dental Plaque, Deep Learning, AI Model, Pediatric Dentistry
Medical subject headings: Dental Plaque, Disease

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

The Difference Between The AI Model and Dentists GroupDiagnostic test
Also called The AI Model and Dentists Group
The clinical feasibility of the best performing model, statistical hypothesis tests are performed that compares the predictions of the AI model with the assessments from three dentists.
Deep Learning ModelsDiagnostic test
Also called The Architecture of Deep Learning Models
DeepLabV3+, Mask R-CNN (Detectron2), YOLOv8, U-Net, Super Vision U-net and U-Net Transformer were trained on 354 images and tested on 79 images.

Study groups in NCT06603233
Study group	Role	What they receive
Deep Learning Models Group	Experimental	As artificial intelligence models, DeepLabV3+, Mask R-CNN (Detectron2), YOLOv8, U-Net, Super Vision U-net and U-Net Transformer models, which are state-of-the-art in semantic segmentation, were selected. Receives: Diagnostic Test: Deep Learning Models
The Difference Between The AI Model (U-Net Transformer) and Dentists Group	Active comparator	Using the prior knowledge (α = 0.05, β = 0.2) and an effect size of 0.61, the actual power of the comparison between the AI model (U-Net Transformer) and dentists on 34 test images is at least 80%, which is deemed sufficient. Therefore, randomly selected 35 images on the test dataset were labeled by three dentists without seeing the ground truth and were predicted by the AI model. Then, the intersection over union (IoU) score of these labeled and predicted images were calculated. The IoU score, which computes the ratio between the intersection and the union of two sets, is commonly used to evaluate the accuracy of prediction on semantic segmentation. To confirm clinical feasibility, three t-tests, which evaluates the difference between the means of two variables, were applied to IoU scores of dentists and IoU scores of the AI model and a p value < .05 was considered statistically significant. Receives: Diagnostic Test: The Difference Between The AI Model and Dentists Group

What is being measured

The primary outcome is the main question the study is designed to answer.

Determination of IoU and Dice Coefficient values among six state-of-the-AI models
The IoU score, which computes the ratio between the intersection and the union of two sets, is commonly used to evaluate the accuracy of prediction on semantic segmentation.
DeepLabV3+, Mask R-CNN (Detectron2), YOLOv8, U-Net, Super Vision U-net and U-Net Transformer were trained on 354 images and tested on 79 images.
IoU and Dice Coefficient values were established among six state-of-the-AI models. As the IoU score increases, the prediction score increases. As the score increases, it becomes more distinctive in determining the model that gives results closest to the correct result.
Measured: two weeks
Prediction scores of the dentists and U-Net Transformer on 35 test images
The prediction scores of the three dentists and the AI model (U-Net Transformer) on 35 test images
Measured: two weeks

Secondary outcomes (1)

T-test results comparing the AI model and the three dentists
T-test results comparing AI model and three dentists Comparison of prediction scores of three dentists and AI model (U-Net Transformer) on 35 test images.
AI model recorded IoU scores on 35 test images according to three dentists. AI model was compared with dentists on Recall, Dice Coefficient, IoU scores and Precision score. These scores were used to distinguish between false positive and true positive. In other words, although there is no plaque on teeth, does AI act as if there is plaque on teeth or does it show the real correct result?
Measured: two weeks

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

1 Jun 20231 Nov 2023

First posted19 Sep 2024
Study start1 Jun 2023
Primary completion1 Nov 2023
Study completion1 Nov 2023

Resources

Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
Publications (4)Papers the sponsor has linked to this study.
Other trials for Dental Plaque (Diagnosis)Studies 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: Interventional
Phase: Phase N/A
Primary purpose: Diagnosis
Allocation: Non-randomised
Intervention model: Factorial
Model description: Using the prior knowledge (α = 0.05, β = 0.2) and an effect size of 0.61, the actual power of the comparison between the AI model and dentists on 34 test images is at least 80%, which is deemed sufficient. Therefore, randomly selected 35 images on the test dataset were labeled by three dentists without seeing the ground truth and were predicted by the AI model. Then, the intersection over union (IoU) score of these labeled and predicted images were calculated. The IoU score, which computes the ratio between the intersection and the union of two sets, is commonly used to evaluate the accuracy of prediction on semantic segmentation. AI model's dental plaque predictions compared with predictions from three dentists.
Masking: Triple blind
Who is masked: Care provider, Investigator, Outcomes assessor
Enrolment: 31 participants (actual)

IDs, dates and publications

ClinicalTrials.gov ID: NCT06603233
Sponsor's protocol ID: AnkaraMedipolU-DNT-BCT-01
Status last verified: Sep 2024
First submitted: 30 Jun 2024
Last updated: 19 Sep 2024
Results posted: No
Data monitoring committee: No
FDA-regulated drug: No
FDA-regulated device: No
Individual data sharing: No

Publications

Liu L, Xu J, Huan Y, Zou Z, Yeh SC, Zheng LR. A Smart Dental Health-IoT Platform Based on Intelligent Hardware, Deep Learning, and Mobile Terminal. IEEE J Biomed Health Inform. 2020 Mar;24(3):898-906. doi: 10.1109/JBHI.2019.2919916. Epub 2019 Jun 7.
PubMed 31180873
You W, Hao A, Li S, Wang Y, Xia B. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health. 2020 May 13;20(1):141. doi: 10.1186/s12903-020-01114-6.
PubMed 32404094
Li S, Guo Y, Pang Z, Song W, Hao A, Xia B, Qin H. Automatic Dental Plaque Segmentation Based on Local-to-Global Features Fused Self-Attention Network. IEEE J Biomed Health Inform. 2022 May;26(5):2240-2251. doi: 10.1109/JBHI.2022.3141773. Epub 2022 May 5.
PubMed 35015655
Tez BC, Guzel Y, Kiziltan Eliacik BB, Aydin Z. Deep-Learning-Based AI-Model for Predicting Dental Plaque in the Young Permanent Teeth of Children Aged 8-13 Years. Children (Basel). 2025 Apr 7;12(4):475. doi: 10.3390/children12040475.
PubMed 40310101

Want to know more?

The sponsor has not listed a contact. The official record may have more details.

Talk to your doctor first and take this page with you. Only the study team can confirm whether you can join.

Questions to ask the study team

What is the study trying to find out?
What would I need to do, and how often would I visit?
Could I receive a placebo instead of the treatment?
What are the possible risks and benefits for me?
Will it cost me anything? Are travel or time paid for?
Can I keep seeing my own doctor during the study?
What happens when the study ends?

See locations

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Individualized Management of Plaque-Related Gingival Inflammation in Routine Dental Practice
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Recruiting
The Study to Evaluate the Plaque Control and Anti-Gingivitis Efficacy of a Toothpaste in Chinese Population
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Not yet recruiting
Comparative Evaluation of the Anti-Plaque and Anti-Gingivitis Effects of Nanobiofusion Gingival Gel (NBF), Placebo Gel, and Commercial Toothpaste
Gingivitis, Healthy Adult

All Dental Plaque (Diagnosis) trials

Study at a glance

From the registry record, last updated by the sponsor on 19 Sep 2024. Recruitment status is as the sponsor reported it: confirm with the study team.

Official title: Microbial Dental Plaque Analysis in Young Permanent Teeth Using Deep Learning in Children Aged 8-13 Years
ClinicalTrials.gov ID: NCT06603233
Acronym / sponsor ID: AnkaraMedipolU-DNT-BCT-01
Conditions: Dental Plaque (Diagnosis), Deep Learning
Keywords: Dental Plaque, Deep Learning, AI Model, Pediatric Dentistry
Study type: Interventional
Recruitment status: Completed
Enrolment: 31 participants (actual)
Lead sponsor: Ankara Medipol UniversityUniversity, hospital or other
Study start: 1 Jun 2023 (actual)
Primary completion: 1 Nov 2023 (actual)
Study completion: 1 Nov 2023 (actual)
First posted: 19 Sep 2024 (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.

Detailed description

Study design

Primary purpose: Diagnosis
Allocation: Non-randomised
Intervention model: Factorial
Masking: Triple blind (care provider, investigator, outcomes assessor)
Number of arms: 2
Model description: Using the prior knowledge (α = 0.05, β = 0.2) and an effect size of 0.61, the actual power of the comparison between the AI model and dentists on 34 test images is at least 80%, which is deemed sufficient. Therefore, randomly selected 35 images on the test dataset were labeled by three dentists without seeing the ground truth and were predicted by the AI model. Then, the intersection over union (IoU) score of these labeled and predicted images were calculated. The IoU score, which computes the ratio between the intersection and the union of two sets, is commonly used to evaluate the accuracy of prediction on semantic segmentation. AI model's dental plaque predictions compared with predictions from three dentists.

Arm	Type	Receives
Deep Learning Models Group	Experimental	Diagnostic Test: Deep Learning ModelsAs artificial intelligence models, DeepLabV3+, Mask R-CNN (Detectron2), YOLOv8, U-Net, Super Vision U-net and U-Net Transformer models, which are state-of-the-art in semantic segmentation, were selected.
The Difference Between The AI Model (U-Net Transformer) and Dentists Group	Active comparator	Diagnostic Test: The Difference Between The AI Model and Dentists GroupUsing the prior knowledge (α = 0.05, β = 0.2) and an effect size of 0.61, the actual power of the comparison between the AI model (U-Net Transformer) and dentists on 34 test images is at least 80%, which is deemed sufficient. Therefore, randomly selected 35 images on the test dataset were labeled by three dentists without seeing the ground truth and were predicted by the AI model. Then, the intersection over union (IoU) score of these labeled and predicted images were calculated. The IoU score, which computes the ratio between the intersection and the union of two sets, is commonly used to evaluate the accuracy of prediction on semantic segmentation. To confirm clinical feasibility, three t-tests, which evaluates the difference between the means of two variables, were applied to IoU scores of dentists and IoU scores of the AI model and a p value < .05 was considered statistically significant.

Who may qualify

Ages: 8 to 13 years
Sex: All
Healthy volunteers: Accepted
Age groups: Child

Key inclusion criteria (1 listed)

Anterior young permanent teeth

Key exclusion criteria (5 listed)

Anterior young permanent teeth exhibiting disruptions in enamel tissue integrity such as decay
Hypoplasia, hypomineralization
Restored and prosthetically treated teeth
Young permanent teeth located in the posterior region
Primary teeth

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
The Difference Between The AI Model and Dentists GroupAlso: The AI Model and Dentists GroupThe clinical feasibility of the best performing model, statistical hypothesis tests are performed that compares the predictions of the AI model with the assessments from three dentists.	Diagnostic test	The Difference Between The AI Model (U-Net Transformer) and Dentists Group
Deep Learning ModelsAlso: The Architecture of Deep Learning ModelsDeepLabV3+, Mask R-CNN (Detectron2), YOLOv8, U-Net, Super Vision U-net and U-Net Transformer were trained on 354 images and tested on 79 images.	Diagnostic test	Deep Learning Models Group

Primary outcome

Determination of IoU and Dice Coefficient values among six state-of-the-AI models
Time frame: two weeks
The IoU score, which computes the ratio between the intersection and the union of two sets, is commonly used to evaluate the accuracy of prediction on semantic segmentation. DeepLabV3+, Mask R-CNN (Detectron2), YOLOv8, U-Net, Super Vision U-net and U-Net Transformer were trained on 354 images and tested on 79 images. IoU and Dice Coefficient values were established among six state-of-the-AI models. As the IoU score increases, the prediction score increases. As the score increases, it becomes more distinctive in determining the model that gives results closest to the correct result.
Prediction scores of the dentists and U-Net Transformer on 35 test images
Time frame: two weeks
The prediction scores of the three dentists and the AI model (U-Net Transformer) on 35 test images

1 secondary outcome

T-test results comparing the AI model and the three dentists
Time frame: two weeks

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 19 Sep 2024.
Background: PubMed 31180873Liu L, Xu J, Huan Y, Zou Z, Yeh SC, Zheng LR. A Smart Dental Health-IoT Platform Based on Intelligent Hardware, Deep Learning, and Mobile Terminal. IEEE J Biomed Health Inform. 2020 Mar;24(3):898-906. doi: 10.1109/JBHI.2019.2919916. Epub 2019 Jun 7.
Background: PubMed 32404094You W, Hao A, Li S, Wang Y, Xia B. Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments. BMC Oral Health. 2020 May 13;20(1):141. doi: 10.1186/s12903-020-01114-6.
Background: PubMed 35015655Li S, Guo Y, Pang Z, Song W, Hao A, Xia B, Qin H. Automatic Dental Plaque Segmentation Based on Local-to-Global Features Fused Self-Attention Network. IEEE J Biomed Health Inform. 2022 May;26(5):2240-2251. doi: 10.1109/JBHI.2022.3141773. Epub 2022 May 5.
Derived from this study: PubMed 40310101Tez BC, Guzel Y, Kiziltan Eliacik BB, Aydin Z. Deep-Learning-Based AI-Model for Predicting Dental Plaque in the Young Permanent Teeth of Children Aged 8-13 Years. Children (Basel). 2025 Apr 7;12(4):475. doi: 10.3390/children12040475.
reference 11Link provided by the sponsor.
Reference 13Link provided by the sponsor.
reference 16Link provided by the sponsor.
reference 18Link provided by the sponsor.
reference 19Link provided by the sponsor.
reference 20Link provided by the sponsor.
Individual participant dataSharing planned: No

Related trials for Dental Plaque (Diagnosis)

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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.

Banu Çiçek Tez, Ph.D principal investigator

Ankara Medipol University

1 site in 1 country: see every site and its status.

Questions about this trial

What is this clinical trial studying?

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 1 inclusion and 5 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 19 Sep 2024. 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?

Determination of IoU and Dice Coefficient values among six state-of-the-AI models (time frame: two weeks). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT06603233, which also lists 4 linked publications.

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 19 Sep 2024.