Deep Learning Framework for Classification, 3D Segmentation & Visualization of C-shaped Canals (NCT07697378) | Clinical Trial Compass
Not Yet RecruitingNot Applicable
Deep Learning Framework for Classification, 3D Segmentation & Visualization of C-shaped Canals
112 participantsStarted 2026-09-05
Plain-language 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?
Who can participate
Age range
18 Years – 60 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
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.
Questions worth asking your doctor
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
1Based on my diagnosis and history, is this trial worth exploring for me — or is there a standard treatment we should try first?
2What does this trial's phase tell us about how much is already known about its safety and benefit?
3What would taking part actually involve for me — visits, tests, time, and travel?
4What are the known and possible risks or side effects I should weigh, and how would they be monitored?
5If this trial isn't the right fit, what other options or trials would you suggest I look into?
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
Questions for the trial coordinator
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
1What does taking part actually involve week to week — how many visits, where, and how long does each one take?
2What costs are covered by the study, and what might I have to pay for myself, including travel, parking, or time off work?
3What happens during screening, and what happens if the study team confirms I don't meet the criteria after those tests?
4Who pays for the scans, blood work, and other tests the trial requires — the study, my insurance, or me?
5How will being in the trial affect my regular care, and will my own doctor stay informed and involved?
6Can I leave the trial at any point if I change my mind, and what would happen to my care if I do?
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
What they're measuring
1
Develop a deep learning framework for Automated Segmentation, classification of C- shaped canals.