FASTER RCNN: train artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy
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?
Eligibility
Sex
ALL
Min age
4 Years
Max age
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.
Primary outcome measure(s)
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.
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
We use cookies to analyse site traffic and improve your experience. With your consent, we may also use cookies for advertising. You can change your choice at any time.