Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence

February 4, 2026 updated by: Rawda Hesham Abd ElAziz, Cairo University

Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence - A Diagnostic Clinical Study

Using a sequence of bitewing radiographs, Artificial intelligence assists in identifying interproximal caries. For the identification of dental caries in bitewing, periapical, and panoramic radiographs, a trained deep learning network will be created This study aimed to investigate the reliability of a novel Artificial Intelligence model based on deep learning in the detection of Proximal Caries using Digital Bitewing Radiographs. (BW).

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Actual)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Egypt
      • Cairo, Egypt, 11331
        • Ain Shams University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

Patients attending the Faculty dental out patient clinic who required bitewing radiographic examination for routine diagnosis or treatment planning.

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Group 1 Artificial Intelligence Deep learning that is applied in Diagnosis of the proximal Caries
Diagnostic test: Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal Caries
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.
Group 2 : Digital Bitewing manually annotated by human experts
Diagnostic test: Manual annotation of Digital Bitewing Radiograph by human experts
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
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
cross-sectional assessment at baseline, with no follow-up period

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Cairo University

Collaborators

Ain Shams University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 15, 2023

Primary Completion (Actual)

September 15, 2025

Study Completion (Actual)

December 1, 2025

Study Registration Dates

First Submitted

February 4, 2026

First Submitted That Met QC Criteria

February 4, 2026

First Posted (Actual)

February 11, 2026

Study Record Updates

Last Update Posted (Actual)

February 11, 2026

Last Update Submitted That Met QC Criteria

February 4, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CONS_ Rad 1180

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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