Artifical Intelligence, Dental Caries (Diagnosis), Intraoral Images
Conditions
Keywords
artificial intelligence, dental caries, diagnosis, intraoral images
Brief 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?
Interventions
DIAGNOSTIC_TESTFASTER RCNN
train artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy
Sponsors
Cairo University
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
4 Years to 12 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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 | one year | Diagnostic accuracy of index tests will be determined, including sensitivity, specificity, overall accuracy, positive and negative predictive values and ROC curve analysis. |
Countries
Egypt
Outcome results
None listed