Dental PlaqueNCT06760104
Comparative Accuracy of AI Models and Clinical Assessment for Dental Plaque Detection in Children
An observational study of Dental Plaque, run by Naema Ahmed at 1 site in Egypt.
Trial summary
This diagnostic accuracy study aims to evaluate the effectiveness of various artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments performed by dentists among children. Read more about this study
- Who is it for?
- Ages 7 to 12 years
- Conditions the study is for
- Dental Plaque
- What is being tested?
- Dental Plaque Detection Using AI Models
- How many people
- 323 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Jan 2025to 30 Dec 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 7 to 12 years
- All sexes
- Child
Study population: Children from 7 to 12 years old.
Who may be able to join
- .Study participants: Children within age range (7-12) years old. .Teeth without metal crowns or amalgam restoration.
Who cannot join
- Children with developmental enamel defects
- Children who are unwilling to cooperate or who has mental retardation and are prohibited from having their images taken. .Children who's their legal guardians will not approve to participate in the study.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Egypt1 site
Cairo University
Cairo, 11511
cairo universitty, 0020238355275, naema.altrablsi@dentistry.cu.edu.eg
Naema Ahmed
About this study
Official title: Accuracy of Dental Plaque Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Clinical Assessment Among a Group of Children: A Diagnostic Accuracy Study.
This diagnostic accuracy study aims to evaluate the effectiveness of various artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments performed by dentists among children. The study seeks to determine the accuracy, sensitivity, specificity, and overall performance of AI technologies in identifying dental plaque. study study Design: Observational study
Read the detailed description
Study Title:
Accuracy of Dental Plaque Detection from Intraoral Images Using Different Artificial Intelligence Models Versus Clinical Assessment Among a Group of Children: A Diagnostic Accuracy Study
Study Overview:
This observational diagnostic accuracy study is designed to evaluate the performance of multiple artificial intelligence (AI) models in detecting dental plaque from intraoral images, compared to traditional clinical assessments conducted by qualified dentists. The primary focus is on pediatric patients, as early detection and management of dental plaque are crucial for maintaining oral health in children.
Background and Rationale:
Dental plaque is a biofilm that forms on teeth and can lead to caries and periodontal disease if not properly managed. Traditional methods of plaque detection rely on visual assessments by dental professionals, which can be subjective and may vary in accuracy. Recent advancements in AI and image processing present an opportunity to enhance the detection and quantification of dental plaque through intraoral images, potentially providing a more objective and efficient assessment tool.
Objectives:
To compare the accuracy of AI models in detecting dental plaque against clinical assessments.
To determine the sensitivity, specificity, and overall diagnostic performance of the AI technologies.
To analyze the potential for AI models to be integrated into routine dental examinations for pediatric patients.
Methodology:
Participants: A sample of pediatric patients will be recruited, ensuring a diverse representation of various demographics and dental health statuses.
Image Acquisition: Intraoral images will be captured using standardized imaging protocols to ensure consistency. High-resolution images will be obtained under controlled conditions to minimize variability.
AI Models: Various AI algorithms, including convolutional neural networks (CNNs) and deep learning techniques, will be trained using a dataset of annotated intraoral images. These models will be evaluated based on their ability to identify and quantify dental plaque.
Clinical Assessment: Trained dentists will perform clinical examinations using standard plaque indices to assess the presence and severity of dental plaque in the same cohort of children.
Data Analysis: Statistical methods will be employed to compare the diagnostic accuracy of AI models with clinical assessments, including calculations of sensitivity, specificity, positive predictive value, and negative predictive value.
Expected Outcomes:
The study aims to elucidate the role of AI in enhancing the detection of dental plaque in children, potentially leading to improved preventive care and treatment strategies. The findings may also contribute to the development of AI-assisted tools for dental practitioners.
Ethical Considerations:
This study will adhere to ethical guidelines, ensuring informed consent is obtained from legal guardians of pediatric participants. Approval from the relevant institutional review board (IRB) will be secured prior to the commencement of the study
- Conditions
- Dental Plaque
- Being tested
- Dental Plaque Detection Using AI Models
- Medical subject headings
- Dental Plaque
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Dental Plaque Detection Using AI ModelsDiagnostic test
- AI Model Analysis: Description: Intraoral images of participants will be captured using standardized imaging techniques. These images will then be analyzed using various artificial intelligence models specifically designed for detecting dental plaque. The AI models will process the images to identify and quantify the presence of dental plaque.
- Clinical Assessment:
Description: A qualified dentist will perform a traditional clinical examination of the participants to assess dental plaque using standard examination techniques. This will serve as the reference standard against which the AI models will be compared.
Study Procedures Image Acquisition: Intraoral images will be taken of each participant using [ intraoral camera].
AI Model Evaluation: The captured images will be analyzed using different AI algorithms, which may include.
| Study group | Role | What they receive |
|---|---|---|
| intraoral images for Children with Dental Plaque for assessment by dentist | Intervention Overview: Participants will undergo intraoral imaging using [intraoral camera]. Intervention Overview: A trained dentist or dental hygienist will conduct a clinical assessment of each child's dental plaque levels using standard clinical criteria. Assessment Method: The clinical assessment will involve visual inspection and may use plaque index to evaluate the amount of plaque present. Data Collection and Analysis: Outcome Measures: The results from the AI models and clinical assessments will be compared to calculate diagnostic accuracy metrics, such as sensitivity, specificity, positive predictive value, and negative predictive value. | |
| intraoral images for Children with Dental Plaque for assessment by AI models | Intervention Overview: Participants will undergo intraoral imaging using [intraoral camera].
AI Models: The images will be analyzed using different AI models designed for dental plaque detection.
Data Collection and Analysis:
Outcome Measures: The results from the AI models and clinical assessments will be compared to calculate diagnostic accuracy metrics, such as sensitivity, specificity, positive predictive value, and negative predictive value. Receives: Diagnostic Test: Dental Plaque Detection Using AI Models |
What is being measured
The primary outcome is the main question the study is designed to answer.
accuracy of dental plaque detection
The primary outcome measure evaluates the diagnostic accuracy of different artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments.
Measured: primary outcome will be assessed at Baseline Prior to any intervention, intraoral images will be captured and assessed using AI models and clinical evaluation.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jan 2025
- Study start1 Jan 2025 (estimated)
- Primary completion30 Dec 2025 (estimated)
- Study completion30 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Dental PlaqueStudies 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
- Other
- Time perspective
- Prospective
- Enrolment
- 323 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Naema Ahmed
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Naema Ahmed, Cairo University
- Investigators
- Cairo University, Study director, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06760104
- Sponsor's protocol ID
- OP7-1-1
- Status last verified
- Jan 2025
- First submitted
- 22 Dec 2024
- Last updated
- 6 Jan 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Accuracy of Dental Plaque Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Clinical Assessment Among a Group of Children: A Diagnostic Accuracy Study.
- ClinicalTrials.gov ID
- NCT06760104
- Acronym / sponsor ID
- OP7-1-1
- Conditions
- Dental Plaque
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 323 participants (estimated)
- Lead sponsor
- Naema AhmedUniversity, hospital or other
- Study start
- 1 Jan 2025 (estimated)
- Primary completion
- 30 Dec 2025 (estimated)
- Study completion
- 30 Dec 2025 (estimated)
- First posted
- 6 Jan 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 diagnostic accuracy study aims to evaluate the effectiveness of various artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments performed by dentists among children. The study seeks to determine the accuracy, sensitivity, specificity, and overall performance of AI technologies in identifying dental plaque. study study Design: Observational study
Detailed description
Study Title:
Accuracy of Dental Plaque Detection from Intraoral Images Using Different Artificial Intelligence Models Versus Clinical Assessment Among a Group of Children: A Diagnostic Accuracy Study
Study Overview:
This observational diagnostic accuracy study is designed to evaluate the performance of multiple artificial intelligence (AI) models in detecting dental plaque from intraoral images, compared to traditional clinical assessments conducted by qualified dentists. The primary focus is on pediatric patients, as early detection and management of dental plaque are crucial for maintaining oral health in children.
Background and Rationale:
Dental plaque is a biofilm that forms on teeth and can lead to caries and periodontal disease if not properly managed. Traditional methods of plaque detection rely on visual assessments by dental professionals, which can be subjective and may vary in accuracy. Recent advancements in AI and image processing present an opportunity to enhance the detection and quantification of dental plaque through intraoral images, potentially providing a more objective and efficient assessment tool.
Objectives:
To compare the accuracy of AI models in detecting dental plaque against clinical assessments.
To determine the sensitivity, specificity, and overall diagnostic performance of the AI technologies.
To analyze the potential for AI models to be integrated into routine dental examinations for pediatric patients.
Methodology:
Participants: A sample of pediatric patients will be recruited, ensuring a diverse representation of various demographics and dental health statuses.
Image Acquisition: Intraoral images will be captured using standardized imaging protocols to ensure consistency. High-resolution images will be obtained under controlled conditions to minimize variability.
AI Models: Various AI algorithms, including convolutional neural networks (CNNs) and deep learning techniques, will be trained using a dataset of annotated intraoral images. These models will be evaluated based on their ability to identify and quantify dental plaque.
Clinical Assessment: Trained dentists will perform clinical examinations using standard plaque indices to assess the presence and severity of dental plaque in the same cohort of children.
Data Analysis: Statistical methods will be employed to compare the diagnostic accuracy of AI models with clinical assessments, including calculations of sensitivity, specificity, positive predictive value, and negative predictive value.
Expected Outcomes:
The study aims to elucidate the role of AI in enhancing the detection of dental plaque in children, potentially leading to improved preventive care and treatment strategies. The findings may also contribute to the development of AI-assisted tools for dental practitioners.
Ethical Considerations:
This study will adhere to ethical guidelines, ensuring informed consent is obtained from legal guardians of pediatric participants. Approval from the relevant institutional review board (IRB) will be secured prior to the commencement of the study
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| intraoral images for Children with Dental Plaque for assessment by dentist | – | –Intervention Overview: Participants will undergo intraoral imaging using [intraoral camera]. Intervention Overview: A trained dentist or dental hygienist will conduct a clinical assessment of each child's dental plaque levels using standard clinical criteria. Assessment Method: The clinical assessment will involve visual inspection and may use plaque index to evaluate the amount of plaque present. Data Collection and Analysis: Outcome Measures: The results from the AI models and clinical assessments will be compared to calculate diagnostic accuracy metrics, such as sensitivity, specificity, positive predictive value, and negative predictive value. |
| intraoral images for Children with Dental Plaque for assessment by AI models | – | Diagnostic Test: Dental Plaque Detection Using AI ModelsIntervention Overview: Participants will undergo intraoral imaging using [intraoral camera]. AI Models: The images will be analyzed using different AI models designed for dental plaque detection. Data Collection and Analysis: Outcome Measures: The results from the AI models and clinical assessments will be compared to calculate diagnostic accuracy metrics, such as sensitivity, specificity, positive predictive value, and negative predictive value. |
Who may qualify
- Ages
- 7 to 12 years
- Sex
- All
- Age groups
- Child
- Study population
- Children from 7 to 12 years old.
Key inclusion criteria (1 listed)
- .Study participants: Children within age range (7-12) years old. .Teeth without metal crowns or amalgam restoration.
Key exclusion criteria (2 listed)
- Children with developmental enamel defects
- Children who are unwilling to cooperate or who has mental retardation and are prohibited from having their images taken. .Children who's their legal guardians will not approve to participate in the study.
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 |
|---|---|---|
| Dental Plaque Detection Using AI Models1. AI Model Analysis: Description: Intraoral images of participants will be captured using standardized imaging techniques. These images will then be analyzed using various artificial intelligence models specifically designed for detecting dental plaque. The AI models will process the images to identify and quantify the presence of dental plaque. 2. Clinical Assessment: Description: A qualified dentist will perform a traditional clinical examination of the participants to assess dental plaque using standard examination techniques. This will serve as the reference standard against which the AI models will be compared. Study Procedures Image Acquisition: Intraoral images will be taken of each participant using [ intraoral camera]. AI Model Evaluation: The captured images will be analyzed using different AI algorithms, which may include. | Diagnostic test | intraoral images for Children with Dental Plaque for assessment by AI models |
Primary outcome
accuracy of dental plaque detection
Time frame: primary outcome will be assessed at Baseline Prior to any intervention, intraoral images will be captured and assessed using AI models and clinical evaluation.
The primary outcome measure evaluates the diagnostic accuracy of different artificial intelligence models in detecting dental plaque from intraoral images compared to clinical assessments.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jan 2025.
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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.
Cairo University study director
Cairo 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 Dental Plaque, run by Naema Ahmed at 1 site in Egypt.
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 2 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: opening soon, as of the sponsor's last update on 6 Jan 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 Egypt. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Naema Altrablsi (00201152442411, naema.altrablsi@dentistry.cu.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
accuracy of dental plaque detection (time frame: primary outcome will be assessed at Baseline Prior to any intervention, intraoral images will be captured and assessed using AI models and clinical evaluation.). 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 6 Jan 2025.