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

NCT07697378 | Not Yet Recruiting

• 1 other identifier: AI in C-Shaped canals
• Study Type: observational
• Target: 112
• Locations: 0 countries
• Sites: N/A

Brief Summary

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?

Conditions

C-shaped Root Canal

Outcome Measures

Primary Outcomes (1)

• Develop a deep learning framework for Automated Segmentation, classification of C- shaped canals.

  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

Eligibility Criteria

• Age: 18 Years - 60 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Cairo University: lead

Biospecimen

Retention: SAMPLES WITHOUT DNA

CBCT scans

Central Study Contacts

Mai Mohamed Safei Eldin Sayed, PhD candidate

CONTACT

0201101733332; Mai.safei@dentistry.cu.edu.eg

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PhD candidate

Study Record Dates

• First Submitted: July 5, 2026
• First Posted: July 13, 2026
• Study Start (Estimated): September 5, 2026
• Primary Completion (Estimated): September 1, 2027
• Study Completion (Estimated): October 1, 2027
• Last Updated: July 13, 2026

Record last verified: 2026-03