Deep Learning Framework for Classification, 3D Segmentation & Visualization of C-shaped Canals (AI)

July 5, 2026 updated by: Mai Mohamed Safei Eldin Sayed, Cairo University

Diagnostic Accuracy of a Deep Learning Framework for Automated Classification, 3D Segmentation and Comprehensive Visualization of C-shaped Root Canal Architecture From Cone-Beam Computed Tomography

The goal of this retrospective diagnostic accuracy study is to develop and validate a deep learning framework for the automated classification, three-dimensional (3D) segmentation, and visualization of C-shaped root canal anatomy using cone-beam computed tomography (CBCT) scans in adults with C-shaped root canals.

The main questions it aims to answer are:

Can a deep learning model accurately classify C-shaped root canal configurations from CBCT images? Can the model precisely segment the complex 3D anatomy of C-shaped root canals, including fins, webs, and isthmuses, with accuracy comparable to expert endodontists? Can the automated framework improve the efficiency and clinical utility of diagnosing and visualizing C-shaped root canal anatomy?

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Not yet recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Detailed Description

The framework is designed to identify C-shaped canal configurations and accurately segment their complex anatomical features, including fins, webs, and isthmuses.

Index test:

Deep Learning Model Design for Automated Classification and Segmentation

Stage 1: Tooth Localization:

  • Objective: To identify and segment the target molar (primarily mandibular second molars) from the full CBCT volume.
  • Architecture: An Attention U-Net based architecture will be explored, known for its ability to focus on important regions and efficiently process dental descriptors.
  • Output: A cropped Region of Interest (ROI) containing the tooth of interest, reducing computational load for subsequent stages.

Stage 2: C-shaped Root Canal Architecture Classification and Segmentation:

  • Objective: To precisely delineate the C-shaped root canal system, including the main canal lumen, fins, webs, and isthmuses, and to classify its specific type (e.g., C1, C2, C3, C4, C5) based on established criteria (e.g., Fan's classification).
  • Architecture: Advanced 3D U-Net variants will be explored, given their proven efficacy in medical image segmentation and ability to capture fine details.

  • Optimization: Models will be trained using robust optimizers (e.g., ADAM) with a managed learning rate schedule. Early stopping criteria will be implemented based on validation set performance to prevent overfitting.

    3D Reconstruction and Advanced Visualization Pipeline 3D Model Generation:

  • Conversion: Segmented 3D masks will be converted into standard 3D file formats, such as Standard Triangle Language (STL), ensuring interoperability with various software and 3D printing platforms.

Interactive Visualization Development:

● Software/Libraries: Open-source libraries like Open3D will be explored for interactive rendering and development of clinical utility features.

Performance Evaluation and Validation

Quantitative Metrics:

  • Segmentation: Dice Similarity Coefficient (DSC), Hausdorff Distance (HD) and Intersection over Union (IoU) will be used to assess spatial overlap and boundary agreement.
  • Classification: Accuracy, Precision, Recall, F1-score, and Area Under the Curve (AUC) will evaluate the model's ability to correctly categorize C-shaped canal types.

Clinical Utility and Efficiency Assessment:

  • Qualitative Evaluation: Experienced endodontists will qualitatively assess the practical applicability and accuracy of the segmented 3D models for diagnosis, treatment planning, and identification of critical anatomical features.
  • Time Efficiency: The time efficiency of the automated framework will be measured and compared to manual segmentation processes.

Reference standard:

  • Expert Annotation: Manual classification and segmentation will be performed by multiple experienced endodontists or dental-maxillofacial radiologists, establishing the "gold standard" ground truth for the dataset. Full manual 3D segmentation, including the intricate architectural features, will be meticulously performed using 3D Slicer software. For 2D annotations, such as those for initial classification tasks or specific cross-sectional views, Roboflow will be utilized.
  • Inter-observer Variability: Inter-observer variability among annotators will be assessed to ensure the consistency and quality of the ground truth.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Estimated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
112

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Retrospective collection of anonymized CBCT scans from the Faculty of Dentistry, Cairo University as well as private radiology service/ dental clinics and publicly available datasets.

Description

Inclusion Criteria:

  • CBCT scans of C- shaped canals of patients aged 18 years or older, with satisfactory image quality, characterized by adequate sharpness, contrast and noise levels, enabling accurate delineation of pulp chambers and root canals. Additionally, the CBCT scans needed to have a field of view (FOV) covering the area of interest.

Exclusion Criteria:

  • Patients younger than 18 years. CBCT scans with poor image quality (e.g., motion artifacts, excessive noise, low contrast, or beam hardening artifacts).

Incomplete field of view that does not include the tooth of interest.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Develop a deep learning framework for Automated Segmentation, classification of C- shaped canals.
Time Frame: 1-3 months
An Attention U-Net based architecture will be explored, known for its ability to focus on important regions and efficiently process dental descriptors.
1-3 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
Cairo University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
September 5, 2026

Primary Completion (Estimated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
September 1, 2027

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
October 1, 2027

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
July 5, 2026

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
July 5, 2026

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
July 13, 2026

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
July 13, 2026

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
July 5, 2026

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
March 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • AI in C-Shaped canals

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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