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

NCT06749743 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 398

Last updated 2025-03-04

No results posted yet for this study

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

DIAGNOSTIC_TEST

FASTER RCNN

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

Sponsors & Collaborators

  • Cairo University

    lead OTHER

Eligibility

Min Age
4 Years
Max Age
12 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-04-30
Primary Completion
2025-12-30
Completion
2025-12-30

Countries

  • Egypt

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06749743 on ClinicalTrials.gov