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Comparative Accuracy of AI Models and Clinical Assessment for Dental Plaque Detection in Children
Sponsor: Naema Ahmed
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
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.
Key Details
Gender
All
Age Range
7 Years - 12 Years
Study Type
OBSERVATIONAL
Enrollment
323
Start Date
2025-01-01
Completion Date
2025-12-30
Last Updated
2025-01-06
Healthy Volunteers
Not specified
Conditions
Interventions
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.
Locations (1)
Cairo University
Cairo, Egypt