Comparative Accuracy of AI Models and Clinical Assessment for Dental Plaque Detection in Children

NCT06760104 | Not Yet Recruiting DMC

• 1 other identifier: OP7-1-1
• Study Type: observational
• Target: 323
• Locations: 1 country
• Sites: 1

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

Conditions

Dental Plaque

Outcome Measures

Primary Outcomes (1)

• 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.

  primary outcome will be assessed at Baseline Prior to any intervention, intraoral images will be captured and assessed using AI models and clinical evaluation.

Study Arms (2)

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.

Diagnostic Test: Dental Plaque Detection Using AI Models

Interventions

Dental Plaque Detection Using AI Models DIAGNOSTIC_TEST

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.

intraoral images for Children with Dental Plaque for assessment by AI models

Eligibility Criteria

• Age: 7 Years - 12 Years
• Sex: all
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Children from 7 to 12 years old.

You may qualify if:

• Study participants: Children within age range (7-12) years old. .Teeth without metal crowns or amalgam restoration.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Naema Ahmed: lead

Study Sites (1)

Cairo University

Cairo, 11511, Egypt

Location

MeSH Terms

Conditions

Dental Plaque

Condition Hierarchy (Ancestors)

Dental Deposits; Tooth Diseases; Stomatognathic Diseases

Study Officials

• Cairo University

  Cairo University

  STUDY DIRECTOR

Central Study Contacts

Naema Altrablsi

CONTACT

00201152442411; naema.altrablsi@dentistry.cu.edu.eg

Hala Mohiey Eldin, Prof. Doctor

CONTACT

00201001459467; hala.mohyeldin@dentistry.cu.edu.eg

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: naemaahmed

Study Record Dates

• First Submitted: December 22, 2024
• First Posted: January 6, 2025
• Study Start: January 1, 2025
• Primary Completion: December 30, 2025
• Study Completion: December 30, 2025
• Last Updated: January 6, 2025

Record last verified: 2025-01

Locations

EG (1)