NCT06749743

Accuracy Of Detection Of Dental Caries From Intraoral Images Using Different ArtificiaI Intelligence Models

Accuracy Of Dental Caries Detection From Intraoral Images Using Different Artificial Intelligence Models Versus Conventional Visual Examination Among A Group Of Children: A Diagnostic Accuracy Study

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 398 (estimated)
• Sponsor: Cairo University · Academic / Other
• Sex: All
• Age: 4 Years – 12 Years
• Healthy volunteers: Not accepted

Summary

The goal of this observational study is to evaluate the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children. The main question it aims to answer is: What is the diagnostic accuracy of different deep learning models in detecting dental caries from intra oral images taken by a professional intra oral camera in children compared to the conventional clinical visual examination?

Conditions

• Dental Caries (Diagnosis)
• Artifical Intelligence
• Intraoral Images

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	FASTER RCNN	train artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy

Timeline

• Start date: 2025-04-30
• Primary completion: 2025-12-30
• Completion: 2025-12-30
• First posted: 2024-12-27
• Last updated: 2025-03-04

Locations

1 site across 1 country: Egypt