Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence

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).

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Proximal Caries Dental Caries Stomatognathic Diseases Tooth Caries Tooth Demineralization Tooth Diseases

Age range

18 year–70 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Ain Shams University

Cairo, 11331, Egypt

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal Caries

Diagnostic Test

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.

Manual annotation of Digital Bitewing Radiograph by human experts

Diagnostic Test

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.

Primary outcomes

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

Sponsors and collaborators

Lead sponsor

Cairo University

Other

Collaborators

Ain Shams University

Registry information

Official study title

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

Important dates

Study start: 2023
Primary completion: 2025
Study completion: 2025
First posted: Feb 11, 2026
Registry last updated: Feb 11, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Completed