Bifid Mandibular CanalBMC-AINCT07114484
Accuracy of AI in Detecting Bifid Mandibular Canal on CBCT: A Diagnostic Accuracy Study
An observational study of Bifid Mandibular Canal, run by Sara Reda Abdelhamid Aboseif at 1 site in Egypt.
Trial summary
The goal of this observational study is to evaluate how accurately a deep learning-based artificial intelligence (AI) model can detect and segment bifid mandibular canals (BMCs) on cone-beam computed tomography (CBCT) scans in Egyptian patients. Read more about this study
- Who is it for?
- Ages 15 years and older
- Conditions the study is for
- Bifid Mandibular Canal
- How many people
- 117 participants
- Where
- 1 siteEgypt
- Study dates
- 6 May 2025to 20 Oct 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 15 years and older
- All sexes
- Child, Adult, Older adult
Study population: CBCT scans were retrospectively collected from the database of the Oral and Maxillofacial Radiology Department at Cairo University. The population included adult patients who had previously undergone CBCT imaging for diagnostic purposes unrelated to bifid mandibular canal suspicion. The cases were selected to include both males and females from a diverse patient base representative of the general outpatient population attending the university dental hospital.
Who may be able to join
- CBCT scans for Egyptian people older than 15 years old (both male and female subjects are to be included)
- The field of view of the selected scans should clearly show at least one mandibular canal completely (from the mandibular foramen to the mental foramen)
Who cannot join
- CBCT images of sub-optimal quality or artifacts/high scatter interfering with the proper assessment of the mandibular canal course.
- CBCT scans showing significant mandibular deformities.
- CBCT scans showing severe bone loss that extends to the mandibular canal.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: The Accuracy of Artificial Intelligence in the Detection of Bifid Mandibular Canal on CBCT: A Diagnostic Accuracy Study
The goal of this observational study is to evaluate how accurately a deep learning-based artificial intelligence (AI) model can detect and segment bifid mandibular canals (BMCs) on cone-beam computed tomography (CBCT) scans in Egyptian patients. This condition is a key anatomical variation that, if missed, may cause surgical complications such as nerve injury.
The study uses previously collected CBCT scans of individuals aged 15 and older from the Oral and Maxillofacial Radiology Department at Cairo University. The scans will be analyzed retrospectively.
Read more
The main questions it aims to answer are:
How closely does the AI model's segmentation of the mandibular canal match the expert manual segmentation?
How accurate is the AI model in identifying the presence or absence of bifid mandibular canals?
Participants are not actively involved. Instead, anonymized CBCT data will be analyzed using the AI model and compared to expert annotations to measure diagnostic performance.
Read the detailed description
This retrospective diagnostic accuracy study aims to evaluate the performance of a novel artificial intelligence (AI) model in the segmentation and detection of bifid mandibular canals (BMC) on cone-beam computed tomography (CBCT) scans. The study will be conducted on anonymized DICOM data collected from a dental radiology archive, with no direct patient contact or intervention.
Study Rationale and Background Accurate identification of bifid mandibular canals is crucial for avoiding complications during oral surgical procedures. Manual identification on CBCT by experts is the current standard, yet it is time-consuming and subject to interobserver variability. Deep learning-based models have the potential to offer automated, reproducible, and efficient alternatives.
AI Model and Methodology The AI model used in this study will be developed using a 3D U-Net architecture implemented in the MONAI framework. Manual segmentation of mandibular canals, including bifid variants, will be performed using 3D Slicer software to serve as ground truth data. The AI model will be trained and validated using this ground truth.
Technical Workflow Data Collection: Retrospective DICOM CBCT images will be extracted and anonymized.
Manual Segmentation: Gold standard segmentation will be performed manually by an expert using 3D Slicer.
Model Development: The 3D U-Net model will be developed and trained using the MONAI framework in Python.
Evaluation: The trained model will be evaluated on unseen test data and compared to manual segmentation using standard performance metrics (Dice Similarity Coefficient, sensitivity, specificity, and accuracy).
Statistical Analysis: The Dice score will be used as the primary metric of agreement. Sample size was calculated based on expected performance values. Confidence intervals were computed, and statistical significance was assessed.
Sample Size Assessment A power calculation was conducted based on the sensitivity and specificity of the model using normal approximation methods. The total sample size was determined to ensure reliable performance evaluation with adequate power and precision.
Data Quality Assurance and Handling A structured process will be employed to ensure that CBCT scans used are of sufficient diagnostic quality.
All manual segmentations will be reviewed by a second radiologist to reduce bias.
All model outputs will be verified against the manual ground truth by a blinded expert.
Data inconsistencies, if present, will be flagged, documented, and excluded from the analysis.
Missing Data Management Given the retrospective nature, no missing data will be filled in or replaced because only complete and usable scans will be included in the study.
Statistical Plan The primary analysis will involve evaluating the overlap between the AI model and expert segmentations using the Dice coefficient. Secondary measures will include the sensitivity and specificity of the AI model in detecting bifid branches. All statistical analyses will be performed using Python libraries such as SciPy and NumPy.
- Conditions
- Bifid Mandibular Canal
- Keywords
- Bifid Mandibular Canal, Cone-Beam Computed Tomography, Artificial Intelligence, Deep Learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| CBCT Images of mandibular canal | Diagnostic Test: Deep Learning-Based Model A deep learning-based diagnostic tool developed by a computer science expert. The model is built upon a convolutional neural network architecture and trained using our study datasets. Alternative Names: Artificial Intelligence (AI) Tool |
What is being measured
The primary outcome is the main question the study is designed to answer.
Segmentation accuracy of deep learning model compared to manual ground truth
Evaluation of the similarity between the AI-generated segmentation of the mandibular canal and the expert manual segmentation using the Dice similarity coefficient.
Measured: immediately after the intervention
Secondary outcomes (1)
Diagnostic accuracy of the AI model in detecting bifid mandibular canals
Assessment of the AI model's ability to correctly classify the presence or absence of bifid mandibular canals on CBCT scans compared to expert radiologists' detection. Binary output will be measured as Present or Absent, and performance will be evaluated using sensitivity, specificity, PPV, NPV, and accuracy.
Measured: immediately after the intervention
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Aug 2025
- Study start6 May 2025
- Primary completion1 Oct 2025
- Study completion20 Oct 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Bifid Mandibular CanalStudies of the same condition, open and under way.
- 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
- 117 participants (actual)
Sponsor and investigators
- Lead sponsor
- Sara Reda Abdelhamid Aboseif
- Sponsor type
- University, hospital or other
- Collaborators
- Cairo University
- Responsible party
- Sponsor-investigator, Sara Reda Abdelhamid Aboseif, Cairo University
- Investigators
- Enas Anter, Ph.D, Study director, Cairo University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07114484
- Sponsor's protocol ID
- OMFR-8-1-1
- Acronym
- BMC-AI
- Status last verified
- Jul 2025
- First submitted
- 31 Jul 2025
- Last updated
- 2 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data, including raw CBCT scans and manual segmentations, will not be shared to protect patient confidentiality and comply with institutional ethics policies. Aggregated performance metrics are available upon reasonable request.
Study at a glance
From the registry record, last updated by the sponsor on 2 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Accuracy of Artificial Intelligence in the Detection of Bifid Mandibular Canal on CBCT: A Diagnostic Accuracy Study
- ClinicalTrials.gov ID
- NCT07114484
- Acronym / sponsor ID
- BMC-AI / OMFR-8-1-1
- Conditions
- Bifid Mandibular Canal
- Keywords
- Bifid Mandibular Canal, Cone-Beam Computed Tomography, Artificial Intelligence, Deep Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 117 participants (actual)
- Lead sponsor
- Sara Reda Abdelhamid AboseifUniversity, hospital or other
- Collaborators
- Cairo University
- Study start
- 6 May 2025 (actual)
- Primary completion
- 1 Oct 2025 (actual)
- Study completion
- 20 Oct 2025 (actual)
- First posted
- 11 Aug 2025 (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 goal of this observational study is to evaluate how accurately a deep learning-based artificial intelligence (AI) model can detect and segment bifid mandibular canals (BMCs) on cone-beam computed tomography (CBCT) scans in Egyptian patients. This condition is a key anatomical variation that, if missed, may cause surgical complications such as nerve injury.
The study uses previously collected CBCT scans of individuals aged 15 and older from the Oral and Maxillofacial Radiology Department at Cairo University. The scans will be analyzed retrospectively.
The main questions it aims to answer are:
How closely does the AI model's segmentation of the mandibular canal match the expert manual segmentation?
How accurate is the AI model in identifying the presence or absence of bifid mandibular canals?
Participants are not actively involved. Instead, anonymized CBCT data will be analyzed using the AI model and compared to expert annotations to measure diagnostic performance.
Detailed description
This retrospective diagnostic accuracy study aims to evaluate the performance of a novel artificial intelligence (AI) model in the segmentation and detection of bifid mandibular canals (BMC) on cone-beam computed tomography (CBCT) scans. The study will be conducted on anonymized DICOM data collected from a dental radiology archive, with no direct patient contact or intervention.
Study Rationale and Background Accurate identification of bifid mandibular canals is crucial for avoiding complications during oral surgical procedures. Manual identification on CBCT by experts is the current standard, yet it is time-consuming and subject to interobserver variability. Deep learning-based models have the potential to offer automated, reproducible, and efficient alternatives.
AI Model and Methodology The AI model used in this study will be developed using a 3D U-Net architecture implemented in the MONAI framework. Manual segmentation of mandibular canals, including bifid variants, will be performed using 3D Slicer software to serve as ground truth data. The AI model will be trained and validated using this ground truth.
Technical Workflow Data Collection: Retrospective DICOM CBCT images will be extracted and anonymized.
Manual Segmentation: Gold standard segmentation will be performed manually by an expert using 3D Slicer.
Model Development: The 3D U-Net model will be developed and trained using the MONAI framework in Python.
Evaluation: The trained model will be evaluated on unseen test data and compared to manual segmentation using standard performance metrics (Dice Similarity Coefficient, sensitivity, specificity, and accuracy).
Statistical Analysis: The Dice score will be used as the primary metric of agreement. Sample size was calculated based on expected performance values. Confidence intervals were computed, and statistical significance was assessed.
Sample Size Assessment A power calculation was conducted based on the sensitivity and specificity of the model using normal approximation methods. The total sample size was determined to ensure reliable performance evaluation with adequate power and precision.
Data Quality Assurance and Handling A structured process will be employed to ensure that CBCT scans used are of sufficient diagnostic quality.
All manual segmentations will be reviewed by a second radiologist to reduce bias.
All model outputs will be verified against the manual ground truth by a blinded expert.
Data inconsistencies, if present, will be flagged, documented, and excluded from the analysis.
Missing Data Management Given the retrospective nature, no missing data will be filled in or replaced because only complete and usable scans will be included in the study.
Statistical Plan The primary analysis will involve evaluating the overlap between the AI model and expert segmentations using the Dice coefficient. Secondary measures will include the sensitivity and specificity of the AI model in detecting bifid branches. All statistical analyses will be performed using Python libraries such as SciPy and NumPy.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| CBCT Images of mandibular canal | – | –Diagnostic Test: Deep Learning-Based Model A deep learning-based diagnostic tool developed by a computer science expert. The model is built upon a convolutional neural network architecture and trained using our study datasets. Alternative Names: Artificial Intelligence (AI) Tool |
Who may qualify
- Ages
- 15 years and older
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- CBCT scans were retrospectively collected from the database of the Oral and Maxillofacial Radiology Department at Cairo University. The population included adult patients who had previously undergone CBCT imaging for diagnostic purposes unrelated to bifid mandibular canal suspicion. The cases were selected to include both males and females from a diverse patient base representative of the general outpatient population attending the university dental hospital.
Key inclusion criteria (2 listed)
- CBCT scans for Egyptian people older than 15 years old (both male and female subjects are to be included)
- The field of view of the selected scans should clearly show at least one mandibular canal completely (from the mandibular foramen to the mental foramen)
Key exclusion criteria (3 listed)
- CBCT images of sub-optimal quality or artifacts/high scatter interfering with the proper assessment of the mandibular canal course.
- CBCT scans showing significant mandibular deformities.
- CBCT scans showing severe bone loss that extends to the mandibular canal.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Segmentation accuracy of deep learning model compared to manual ground truth
Time frame: immediately after the intervention
Evaluation of the similarity between the AI-generated segmentation of the mandibular canal and the expert manual segmentation using the Dice similarity coefficient.
1 secondary outcome
Diagnostic accuracy of the AI model in detecting bifid mandibular canals
Time frame: immediately after the intervention
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 2 Jan 2026.
- Individual participant dataSharing planned: No. Individual participant data, including raw CBCT scans and manual segmentations, will not be shared to protect patient confidentiality and comply with institutional ethics policies. Aggregated performance metrics are available upon reasonable request.
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.
Enas Anter, Ph.D study director
Cairo University
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 Bifid Mandibular Canal, run by Sara Reda Abdelhamid Aboseif 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 2 inclusion and 3 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 2 Jan 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?
Segmentation accuracy of deep learning model compared to manual ground truth (time frame: immediately after the intervention). 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 2 Jan 2026.