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NCT07404007

Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence

Completed Last updated 11 February 2026
What this trial tests

trial testing Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal Caries in Proximal Caries in 2,000 participants. Completed in 1 December 2025.

Timeline
15 January 2023
Primary endpoint
15 September 2025
1 December 2025

Quick facts

Lead sponsorCairo University
StatusCompleted
Study typeOBSERVATIONAL
Enrollment2,000
Start date15 January 2023
Primary completion15 September 2025
Estimated completion1 December 2025
Sites1 location across Egypt

Drugs / interventions tested

Conditions studied

Sponsor

Cairo University

Who can join

Adults 18 to 70, any sex, with Proximal Caries or Tooth Caries. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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).

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.

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Other recruiting trials for Proximal Caries

Currently open trials in the same condition.

Other Cairo University trials

Trials by the same sponsor.

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Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07404007.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing