Accuracy Of Detection Of Dental Caries From Intraoral Images Using Different ArtificiaI Intelligence Models
NCT ID: NCT06749743
Last Updated: 2025-03-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
398 participants
OBSERVATIONAL
2025-04-30
2025-12-30
Brief Summary
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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?
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Detailed Description
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Conditions
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Study Design
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OTHER
CROSS_SECTIONAL
Study Groups
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training group
images used to train the AI models on detection of dental caries from intraoral images.
FASTER RCNN
train artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy
test group
images used to test the accuracy of the AI models in diagnosis of dental caries from intraoral images.
FASTER RCNN
train artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy
Interventions
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FASTER RCNN
train artificial intelligence models ( FASTER RCNN, YOLOY ) to detect dental caries , then test their accuracy
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* Children with any systemic medical condition.
* Parent / child refuse to participate in the study.
* Uncooperative child.
4 Years
12 Years
ALL
No
Sponsors
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Cairo University
OTHER
Responsible Party
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Sherine Tarek Mohamed Elsayed Khaled
principal investigator
Locations
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Cairo university
Giza, Giza Governorate, Egypt
Countries
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Facility Contacts
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mahmoud ahmed Vice President for Graduate Studies and Research, Phd
Role: primary
Other Identifiers
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OP 7-1-1
Identifier Type: -
Identifier Source: org_study_id
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