Root Canal TreatmentNCT07684482
Diagnostic Accuracy of a Deep Learning Framework for Automated Evaluation of Root Canal Obturation Quality From Periapical Radiographs
A study testing Deep learning model in people with Root Canal Treatment and Obturation Quality, run by Cairo University.
Trial summary
This study aims to develop and evaluate an artificial intelligence (AI)-based system that can automatically assess the quality of root canal fillings using dental X-ray images. The AI system will analyze important features of the filling, including its length, uniformity, and shape, and classify the treatment quality… Read more about this study
- Who is it for?
- Ages 18 to 60 years
- Conditions the study is for
- Root Canal Treatment, Obturation Quality
- What is being tested?
- Deep learning model
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 490 participants
- Where
- No sites listed yet
- Study dates
- 1 Aug 2026to 1 Jul 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 60 years
- All sexes
- No healthy volunteers
- Adult
Periapical radiographs of teeth with completed root canal treatment from patients Aged between 18 and 60 years will be included, provided they exhibit satisfactory image quality characterized by adequate sharpness, contrast, and minimal noise, allowing clear visualization of the root canal filling and apical region. The radiographs must enable accurate assessment of obturation quality, including filling length, homogeneity, and taper. Both single-rooted and multi-rooted teeth will be considered to ensure adequate anatomical representation. Radiographs with poor image quality, significant distortion, metallic artifacts, post-core restorations, root resorption, fractures, or incomplete visualization of the apex will be excluded to ensure reliable analysis.
About this study
Official title: Diagnostic Accuracy of a Deep Learning Framework for Automated Classification, Quantitative Assessment and Comprehensive Evaluation of Root Canal Obturation Quality From Periapical Radiographs
This study aims to develop and evaluate an artificial intelligence (AI)-based system that can automatically assess the quality of root canal fillings using dental X-ray images. The AI system will analyze important features of the filling, including its length, uniformity, and shape, and classify the treatment quality as acceptable or needing improvement.
The study will use previously collected, anonymized dental X-ray images of teeth that have received root canal treatment. Experienced dental specialists will evaluate these images to provide a reference standard, which will be compared with the AI system's results.
The goal of this research is to determine whether AI can provide a reliable and consistent method for evaluating root canal treatment outcomes. In the future, such technology may help dentists make more accurate decisions, improve treatment evaluation, and contribute to better patient care.
- Conditions
- Root Canal Treatment, Obturation Quality
- Being tested
- Deep learning model
- Keywords
- artificial intelligence, obturation
- Medical subject headings
- Bites and Stings
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning modelDiagnostic test
This study aims to develop and evaluate an artificial intelligence (AI)-based system that can automatically assess the quality of root canal fillings using dental X-ray images. The AI system will analyze important features of the filling, including its length, uniformity, and shape, and classify the treatment quality as acceptable or needing improvement.
| Study group | Role | What they receive |
|---|---|---|
| Specialist annotation | Other | Not described Receives: Diagnostic Test: Deep learning model |
What is being measured
The primary outcome is the main question the study is designed to answer.
Evaluation of root canal obturation quality from periapical radiographs
Evaluation of root canal obturation quality from periapical radiographs
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jul 2026
- Study start1 Aug 2026 (estimated)
- Primary completion30 Dec 2026 (estimated)
- Study completion1 Jul 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Root Canal TreatmentStudies 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 490 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Bothaina Mahmoud Elbadry, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07684482
- Sponsor's protocol ID
- New Endo 7.1.1
- Status last verified
- Jun 2026
- First submitted
- 29 Jun 2026
- Last updated
- 6 Jul 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 6 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Diagnostic Accuracy of a Deep Learning Framework for Automated Classification, Quantitative Assessment and Comprehensive Evaluation of Root Canal Obturation Quality From Periapical Radiographs
- ClinicalTrials.gov ID
- NCT07684482
- Acronym / sponsor ID
- New Endo 7.1.1
- Conditions
- Root Canal Treatment, Obturation Quality
- Keywords
- artificial intelligence, obturation
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 490 participants (estimated)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- 1 Aug 2026 (estimated)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 1 Jul 2027 (estimated)
- First posted
- 6 Jul 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.
This study aims to develop and evaluate an artificial intelligence (AI)-based system that can automatically assess the quality of root canal fillings using dental X-ray images. The AI system will analyze important features of the filling, including its length, uniformity, and shape, and classify the treatment quality as acceptable or needing improvement.
The study will use previously collected, anonymized dental X-ray images of teeth that have received root canal treatment. Experienced dental specialists will evaluate these images to provide a reference standard, which will be compared with the AI system's results.
The goal of this research is to determine whether AI can provide a reliable and consistent method for evaluating root canal treatment outcomes. In the future, such technology may help dentists make more accurate decisions, improve treatment evaluation, and contribute to better patient care.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Specialist annotation | Other | Diagnostic Test: Deep learning model |
Who may qualify
- Ages
- 18 to 60 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
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 |
|---|---|---|
| Deep learning modelThis study aims to develop and evaluate an artificial intelligence (AI)-based system that can automatically assess the quality of root canal fillings using dental X-ray images. The AI system will analyze important features of the filling, including its length, uniformity, and shape, and classify the treatment quality as acceptable or needing improvement. | Diagnostic test | Specialist annotation |
Primary outcome
Evaluation of root canal obturation quality from periapical radiographs
Time frame: 1 month
Evaluation of root canal obturation quality from periapical radiographs
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jul 2026.
- Individual participant dataSharing planned: Undecided
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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.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
A study testing Deep learning model in people with Root Canal Treatment and Obturation Quality, run by Cairo University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
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: opening soon, as of the sponsor's last update on 6 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
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?
This study is still under way. Results are not available in the source record yet.
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?
Evaluation of root canal obturation quality from periapical radiographs (time frame: 1 month). 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 6 Jul 2026.