Comparative Accuracy of AI Models and Clinical Assessment for Dental Plaque Detection in Children
NCT ID: NCT06760104
Last Updated: 2025-01-06
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
323 participants
OBSERVATIONAL
2025-01-01
2025-12-30
Brief Summary
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Detailed Description
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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
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Study Design
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OTHER
PROSPECTIVE
Study Groups
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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.
No interventions assigned to this group
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.
Dental Plaque Detection Using AI Models
1. 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.
Interventions
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Dental Plaque Detection Using AI Models
1. 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.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* 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.
7 Years
12 Years
ALL
No
Sponsors
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Naema Ahmed
OTHER
Responsible Party
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Naema Ahmed
naemaahmed
Principal Investigators
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Cairo University
Role: STUDY_DIRECTOR
Cairo University
Locations
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Cairo University
Cairo, , Egypt
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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OP7-1-1
Identifier Type: -
Identifier Source: org_study_id
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