Proximal CariesNCT07404007
Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence
An observational study of Proximal Caries and Tooth Caries, run by Cairo University at 1 site in Egypt.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- Proximal Caries, Tooth Caries
- What is being tested?
- Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal Caries and Manual annotation of Digital Bitewing Radiograph by human experts
- How many people
- 2,000 participants
- Where
- 1 siteEgypt
- Study dates
- 15 Jan 2023to 1 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients attending the Faculty dental out patient clinic who required bitewing radiographic examination for routine diagnosis or treatment planning.
Who may be able to join
- Patients having all Permanent premolars and molars (maximum one tooth missing on each side)
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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).
- Conditions
- Proximal Caries, Tooth Caries
- Being tested
- Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal Caries, Manual annotation of Digital Bitewing Radiograph by human experts
- Keywords
- Artificial intelligence, Proximal caries, A Diagnostic accuracy Study
- Medical subject headings
- Dental Caries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal CariesDiagnostic 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 expertsDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| Group 1 Artificial Intelligence Deep learning that is applied in Diagnosis of the proximal Caries | Not described Receives: Diagnostic Test: Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal Caries | |
| Group 2 : Digital Bitewing manually annotated by human experts | Not described Receives: Diagnostic Test: Manual annotation of Digital Bitewing Radiograph by human experts |
What is being measured
The primary outcome is the main question the study is designed to answer.
Reliability of the artificial intelligence model in detecting proximal caries on digital bitewing radiographs
Measured: cross-sectional assessment at baseline, with no follow-up period
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Feb 2026
- Study start15 Jan 2023
- Primary completion15 Sep 2025
- Study completion1 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Proximal CariesStudies of the same condition, open and under way.
- More trials run by Cairo UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Collaborators
- Ain Shams University
- Responsible party
- Principal investigator, Rawda Hesham Abd ElAziz, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07404007
- Sponsor's protocol ID
- CONS_ Rad 1180
- Status last verified
- Feb 2026
- First submitted
- 4 Feb 2026
- Last updated
- 11 Feb 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Detection of Proximal Caries in Bitewing Radiography Using Artificial Intelligence - A Diagnostic Clinical Study
- ClinicalTrials.gov ID
- NCT07404007
- Acronym / sponsor ID
- CONS_ Rad 1180
- Conditions
- Proximal Caries, Tooth Caries
- Keywords
- Artificial intelligence, Proximal caries, A Diagnostic accuracy Study
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,000 participants (actual)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Collaborators
- Ain Shams University
- Study start
- 15 Jan 2023 (actual)
- Primary completion
- 15 Sep 2025 (actual)
- Study completion
- 1 Dec 2025 (actual)
- First posted
- 11 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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 |
| Group 2 : Digital Bitewing manually annotated by human experts | – | Diagnostic Test: Manual annotation of Digital Bitewing Radiograph by human experts |
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients attending the Faculty dental out patient clinic who required bitewing radiographic examination for routine diagnosis or treatment planning.
Key inclusion criteria (1 listed)
- Patients having all Permanent premolars and molars (maximum one tooth missing on each side)
Key exclusion criteria (1 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence (AI): Deep learning that is applied in Diagnosis of the proximal CariesArtificial 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 test | Group 1 Artificial Intelligence Deep learning that is applied in Diagnosis of the proximal Caries |
| Manual annotation of Digital Bitewing Radiograph by human expertsDigital 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. | Diagnostic test | Group 2 : Digital Bitewing manually annotated by human experts |
Primary outcome
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
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Feb 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Proximal Caries and Tooth Caries, run by Cairo University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 11 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 11 Feb 2026.