Accuracy Of Detection Of Dental Caries From Intraoral Images Using Different ArtificiaI Intelligence Models
Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age4-12
SponsorCairo University
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
Child dentition having at least one decayed tooth.
Disqualifiers
Child dentition with developmental enamel defects.
Children with any systemic medical condition.
Parent / child refuse to participate in the study.
Uncooperative child.
Trial population
Any child with at least one decayed tooth present at the time of recruitment at the Pediatric dental department diagnostic center, Faculty of Dentistry, Cairo University.
Trial design
Design model
Other
Time perspective
Cross-sectional
Treatments tested in this trial
FASTER RCNN
Diagnostic testtrain artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy
Treatment groups
398 Participants
are divided into 2 treatment groupsGroup A: training group1 intervention
Group B: test group1 intervention
Trial outcomes
Primary outcomes
1
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 yearSponsors and contacts
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Source ClinicalTrials.gov
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