Ignite Creation Date:
2025-12-24 @ 9:20 PM
Ignite Modification Date:
2025-12-25 @ 7:07 PM
Study NCT ID:
NCT06760104
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-01-06
First Post:
2024-12-22
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Comparative Accuracy of AI Models and Clinical Assessment for Dental Plaque Detection in Children
Sponsor:
Naema Ahmed
Organization:
Study Overview
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.
Status:
NOT_YET_RECRUITING
Status Verified Date:
2025-01
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief 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. 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
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 Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: