Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence
Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence - A Diagnostic Clinical Study
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
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Cairo, Egypt, 11331
- Ain shams university
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients having all Permanent premolars and molars (maximum one tooth missing on each side)
Exclusion Criteria:
- 1-Dental Anomalies →Amelogenesis Imperfecta, Dentinogenesis Imperfecta, taurodontism 2- Severe crowding which prevent visualization of teeth Contacts 3-Orthodontic wires bonded to Enamel of the tooth
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
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Group 1 Artificial Intelligence Deep learning that is applied in Diagnosis of the proximal Caries
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Artificial intelligence was used as a deep-learning diagnostic tool to detect proximal caries on digital bitewing radiographs.
The system analyzed images and generated probability scores and visual markers for suspected lesions.
Its performance was compared with expert examiner diagnoses as the reference standard.
AI results were used for evaluation only and did not influence patient treatment decisions.
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Group 2 : Digital Bitewing manually annotated by human experts
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Digital bitewing radiographs were manually annotated by calibrated human experts to identify the presence and location of proximal caries.
Annotations were performed using standardized diagnostic criteria and dedicated imaging software to mark suspected lesions.
These expert markings served as the reference standard for comparison with the artificial intelligence outputs.
Inter-examiner agreement was assessed, and disagreements were resolved by consensus.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
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Reliability of the artificial intelligence model in detecting proximal caries on digital bitewing radiographs
Time Frame: cross-sectional assessment at baseline, with no follow-up period
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cross-sectional assessment at baseline, with no follow-up period
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- CONS_ Rad 1180
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
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