Root CanalNCT07843992
AI-Based Root Canal Length, Curvature, and Morphology Assessment Using CBCT
An observational study of Root Canal, run by Cairo University.
Trial summary
The primary aim of this study is to develop, evaluate, and validate a deep learning-based software system capable of generating automated, comprehensive clinical reports that detect, segment, and quantify root canal curvature, total tooth length, and morphological configurations in maxillary and mandibular anterior… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Root Canal
- What is being tested?
- AI-Based Deep Learning System for Root Canal Assessment
- How many people
- 155 participants
- Where
- No sites listed yet
- Study dates
- Oct 2026to Oct 2027
- Phase
- Observational
- 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 Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Pre-existing anonymized CBCT DICOM datasets of permanent maxillary and mandibular anterior and premolar teeth obtained from university hospitals and private centers. Eligible datasets will be retrospectively identified according to predefined inclusion and exclusion criteria and will be used for development, validation, and independent testing of the AI-based diagnostic system. No new imaging or intervention will be performed for the purpose of this study.
Who may be able to join
- Pre-existing CBCT DICOM datasets of maxillary \& mandibular anterior \& premolar teeth.
- Permanent teeth with fully developed roots and completely formed apices.
- High-quality CBCT scans with sufficient image resolution and minimal artifacts to permit accurate visualization of the root canal system.
- Teeth with intact root canal anatomy suitable for automated analysis of root canal length, curvature, number of roots, number of root canals, and canal morphology.
- Teeth representing a wide range of normal anatomical variations and root canal configurations to ensure adequate diversity for AI model development and validation.
- DICOM datasets suitable for image preprocessing, annotation, and deep learning analysis.
Who cannot join
- Teeth with incomplete root formation or open apices.
- Teeth that have undergone previous endodontic treatment, retreatment, apexification, regenerative endodontic procedures, or root-end surgery.
- Teeth with extensive coronal destruction, large restorations, metallic posts, intracanal filling materials, or crowns that obscure the root canal anatomy.
- Teeth with root fractures, perforations, internal or external root resorption, severe root dilacerations, or other conditions that interfere with accurate anatomical assessment.
- Teeth associated with extensive periapical lesions, cysts, tumors, or other pathological conditions that significantly alter the normal root canal anatomy.
- Teeth with developmental anomalies affecting root morphology, including fusion, gemination, dens invaginatus, dens evaginatus, taurodontism,
- CBCT datasets with severe motion artifacts, beam-hardening artifacts, metallic artifacts, excessive image noise, or poor image quality that compromises accurate image analysis.
- Duplicate or incomplete DICOM datasets, corrupted image files, or datasets unsuitable for AI processing.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Development and Validation of an AI-Based Deep Learning System for Automated Assessment and Clinical Reporting of Root Canal Length, Curvature, and Morphology in Anterior and Premolar Teeth Using CBCT
The primary aim of this study is to develop, evaluate, and validate a deep learning-based software system capable of generating automated, comprehensive clinical reports that detect, segment, and quantify root canal curvature, total tooth length, and morphological configurations in maxillary and mandibular anterior and premolar teeth using Cone-Beam Computed Tomography (CBCT) datasets
Read the detailed description
Primary goal is to develop and validate the diagnostic performance metrics (e.g., Dice similarity coefficient, sensitivity, accuracy) of an artificial intelligence (AI)-driven tool; deep learning-based software system. That is capable of generating automated, comprehensive clinical reports based on: Automatic segmentation \& measurements of tooth length, root canal curvature, as well as segmentation and classifying morphological configurations in maxillary \& mandibular anterior \& premolar teeth using Cone-Beam Computed Tomography (CBCT) datasets.
It involves a two-phase workflow:
- Development Phase: Deep learning models (like 3D U-Net architectures) are trained on a large, historically anonymized dataset of CBCT scans.
- Validation Phase: To validate the model's segmentations, measurements and processing time for the automated clinical reports against expert reference standards of manual measurements performed by expert endodontists and oral and maxillofacial radiologists
- Conditions
- Root Canal
- Being tested
- AI-Based Deep Learning System for Root Canal Assessment
- Keywords
- Artificial Intelligence, deep learning, root canal
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Deep Learning System for Root Canal AssessmentDiagnostic test
A deep learning-based diagnostic system developed to automatically analyze anonymized CBCT DICOM datasets of maxillary and mandibular anterior and premolar teeth. The system performs automated tooth and root canal segmentation and assesses root canal length, curvature, number of roots and canals, and canal morphology. The AI-generated results will be compared with an expert-derived reference standard to evaluate diagnostic accuracy and agreement. The system also generates a standardized automated clinical report for each evaluated tooth.
| Study group | Role | What they receive |
|---|---|---|
| Anonymized CBCT Datasets | Pre-existing anonymized CBCT DICOM datasets of maxillary and mandibular anterior and premolar teeth meeting the predefined eligibility criteria. The datasets will be used for development, validation, and independent testing of the deep learning-based system for automated assessment of root canal length, curvature, number of roots and canals, and root canal morphology. The independent validation set will include 155 selected teeth. Receives: Diagnostic Test: AI-Based Deep Learning System for Root Canal Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
Tooth-level diagnostic accuracy and agreement of the AI-based system for root canal anatomical assessment
Diagnostic accuracy and agreement of the AI-based system will be assessed at the tooth level by comparing AI-generated measurements and classifications of root canal length, curvature, number of roots and canals, and canal morphology with the reference standard established by a panel of expert endodontists and oral and maxillofacial radiologists. Continuous and categorical outcomes will be evaluated using appropriate agreement and diagnostic accuracy measures.
Measured: through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Sep 2026
- Study startOct 2026 (estimated)
- Primary completionSep 2027 (estimated)
- Study completionOct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Root CanalStudies 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
- Other
- Time perspective
- Retrospective
- Enrolment
- 155 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Cairo University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Eslam Fathy Bakhit Taha, Cairo University
- Investigators
- Ghada El-Hilaly Mohamed Eid, Prof Dr, Study chair, Cairo UniversityEslam fathy, BDS, Principal investigator, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07843992
- Sponsor's protocol ID
- ENDO 19.9.26
- Status last verified
- Sep 2026
- First submitted
- 19 Sep 2026
- Last updated
- 28 Sep 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared with other researchers because the study uses pre-existing anonymized CBCT DICOM datasets, and no plan has been established for external sharing of the individual-level data
Publications
Chen Z, Liu Q, Wang J, Ji N, Gong Y, Gao B. Tooth image segmentation and root canal measurement based on deep learning. Front Bioeng Biotechnol. 2025 Jun 9;13:1565403. doi: 10.3389/fbioe.2025.1565403. eCollection 2025.
PubMed 40552111
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an AI-Based Deep Learning System for Automated Assessment and Clinical Reporting of Root Canal Length, Curvature, and Morphology in Anterior and Premolar Teeth Using CBCT
- ClinicalTrials.gov ID
- NCT07843992
- Acronym / sponsor ID
- ENDO 19.9.26
- Conditions
- Root Canal
- Keywords
- Artificial Intelligence, deep learning, root canal
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 155 participants (estimated)
- Lead sponsor
- Cairo UniversityUniversity, hospital or other
- Study start
- Oct 2026 (estimated)
- Primary completion
- Sep 2027 (estimated)
- Study completion
- Oct 2027 (estimated)
- First posted
- 28 Sep 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.
The primary aim of this study is to develop, evaluate, and validate a deep learning-based software system capable of generating automated, comprehensive clinical reports that detect, segment, and quantify root canal curvature, total tooth length, and morphological configurations in maxillary and mandibular anterior and premolar teeth using Cone-Beam Computed Tomography (CBCT) datasets
Detailed description
Primary goal is to develop and validate the diagnostic performance metrics (e.g., Dice similarity coefficient, sensitivity, accuracy) of an artificial intelligence (AI)-driven tool; deep learning-based software system. That is capable of generating automated, comprehensive clinical reports based on: Automatic segmentation \& measurements of tooth length, root canal curvature, as well as segmentation and classifying morphological configurations in maxillary \& mandibular anterior \& premolar teeth using Cone-Beam Computed Tomography (CBCT) datasets.
It involves a two-phase workflow:
- Development Phase: Deep learning models (like 3D U-Net architectures) are trained on a large, historically anonymized dataset of CBCT scans.
- Validation Phase: To validate the model's segmentations, measurements and processing time for the automated clinical reports against expert reference standards of manual measurements performed by expert endodontists and oral and maxillofacial radiologists
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Anonymized CBCT Datasets | – | Diagnostic Test: AI-Based Deep Learning System for Root Canal AssessmentPre-existing anonymized CBCT DICOM datasets of maxillary and mandibular anterior and premolar teeth meeting the predefined eligibility criteria. The datasets will be used for development, validation, and independent testing of the deep learning-based system for automated assessment of root canal length, curvature, number of roots and canals, and root canal morphology. The independent validation set will include 155 selected teeth. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Pre-existing anonymized CBCT DICOM datasets of permanent maxillary and mandibular anterior and premolar teeth obtained from university hospitals and private centers. Eligible datasets will be retrospectively identified according to predefined inclusion and exclusion criteria and will be used for development, validation, and independent testing of the AI-based diagnostic system. No new imaging or intervention will be performed for the purpose of this study.
Key inclusion criteria (6 listed)
- Pre-existing CBCT DICOM datasets of maxillary \& mandibular anterior \& premolar teeth.
- Permanent teeth with fully developed roots and completely formed apices.
- High-quality CBCT scans with sufficient image resolution and minimal artifacts to permit accurate visualization of the root canal system.
- Teeth with intact root canal anatomy suitable for automated analysis of root canal length, curvature, number of roots, number of root canals, and canal morphology.
- Teeth representing a wide range of normal anatomical variations and root canal configurations to ensure adequate diversity for AI model development and validation.
Key exclusion criteria (8 listed)
- Teeth with incomplete root formation or open apices.
- Teeth that have undergone previous endodontic treatment, retreatment, apexification, regenerative endodontic procedures, or root-end surgery.
- Teeth with extensive coronal destruction, large restorations, metallic posts, intracanal filling materials, or crowns that obscure the root canal anatomy.
- Teeth with root fractures, perforations, internal or external root resorption, severe root dilacerations, or other conditions that interfere with accurate anatomical assessment.
- Teeth associated with extensive periapical lesions, cysts, tumors, or other pathological conditions that significantly alter the normal root canal anatomy.
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 |
|---|---|---|
| AI-Based Deep Learning System for Root Canal AssessmentA deep learning-based diagnostic system developed to automatically analyze anonymized CBCT DICOM datasets of maxillary and mandibular anterior and premolar teeth. The system performs automated tooth and root canal segmentation and assesses root canal length, curvature, number of roots and canals, and canal morphology. The AI-generated results will be compared with an expert-derived reference standard to evaluate diagnostic accuracy and agreement. The system also generates a standardized automated clinical report for each evaluated tooth. | Diagnostic test | Anonymized CBCT Datasets |
Primary outcome
Tooth-level diagnostic accuracy and agreement of the AI-based system for root canal anatomical assessment
Time frame: through study completion, an average of 1 year
Diagnostic accuracy and agreement of the AI-based system will be assessed at the tooth level by comparing AI-generated measurements and classifications of root canal length, curvature, number of roots and canals, and canal morphology with the reference standard established by a panel of expert endodontists and oral and maxillofacial radiologists. Continuous and categorical outcomes will be evaluated using appropriate agreement and diagnostic accuracy measures.
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 28 Sep 2026.
- Results publication: PubMed 40552111Chen Z, Liu Q, Wang J, Ji N, Gong Y, Gao B. Tooth image segmentation and root canal measurement based on deep learning. Front Bioeng Biotechnol. 2025 Jun 9;13:1565403. doi: 10.3389/fbioe.2025.1565403. eCollection 2025.
- Individual participant dataSharing planned: No. Individual participant data will not be shared with other researchers because the study uses pre-existing anonymized CBCT DICOM datasets, and no plan has been established for external sharing of the individual-level data
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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.
Ghada El-Hilaly Mohamed Eid, Prof Dr study chair
Cairo University
Eslam fathy, BDS principal investigator
Cairo University
Questions about this trial
What is this clinical trial studying?
An observational study of Root Canal, 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?
The sponsor lists 6 inclusion and 8 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: opening soon, as of the sponsor's last update on 28 Sep 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?
Contact Eslam fathy, BDS (+201551682022, eslam.fathy@dentistry.cu.edu.eg), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Tooth-level diagnostic accuracy and agreement of the AI-based system for root canal anatomical assessment (time frame: through study completion, an average of 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07843992, which also lists 1 linked publication.
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 28 Sep 2026.