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

N/ANot Yet RecruitingNCT06749743

Cairo University398 enrolled

Overview

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?

Study Type

OBSERVATIONAL

Enrollment

398

Conditions

Dental Caries (Diagnosis)Artifical Intelligence Intraoral Images

Interventions

FASTER RCNNDIAGNOSTIC_TEST

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

Eligibility

Sex: ALLMin age: 4 YearsMax age: 12 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Child dentition having at least one decayed tooth. Exclusion Criteria: * Child dentition with developmental enamel defects. * Children with any systemic medical condition. * Parent / child refuse to participate in the study. * Uncooperative child.

Outcomes

Primary Outcomes

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

Diagnostic accuracy of index tests will be determined, including sensitivity, specificity, overall accuracy, positive and negative predictive values and ROC curve analysis.

Time frame: one year