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

NCT07697378 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 112

Last updated 2026-07-13

No results posted yet for this study

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

Sponsors & Collaborators

  • Cairo University

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
60 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-09-05
Primary Completion
2027-09-01
Completion
2027-10-01

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07697378 on ClinicalTrials.gov