Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence

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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07404007

Enrollment

2000

Registered

2026-02-11

Start date

2023-01-15

Completion date

2025-12-01

Last updated

2026-02-11

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Proximal Caries, Tooth Caries

Keywords

Artificial intelligence, Proximal caries, A Diagnostic accuracy Study

Brief summary

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).

Interventions

DIAGNOSTIC_TESTArtificial 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.

DIAGNOSTIC_TESTManual 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.

Study design

Observational model

CASE_CONTROL

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 70 Years

Healthy volunteers

Yes

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

Design outcomes

Primary

Measure	Time frame
Reliability of the artificial intelligence model in detecting proximal caries on digital bitewing radiographs	cross-sectional assessment at baseline, with no follow-up period

Countries

Egypt

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 12, 2026