Diagnostic Accuracy of a Deep Learning Framework for Automated Evaluation of Root Canal Obturation Quality From Periapical Radiographs

June 29, 2026 updated by: Bothaina Mahmoud Elbadry, Cairo University

Diagnostic Accuracy of a Deep Learning Framework for Automated Classification, Quantitative Assessment and Comprehensive Evaluation of Root Canal Obturation Quality From Periapical Radiographs

This study aims to develop and evaluate an artificial intelligence (AI)-based system that can automatically assess the quality of root canal fillings using dental X-ray images. The AI system will analyze important features of the filling, including its length, uniformity, and shape, and classify the treatment quality as acceptable or needing improvement.

The study will use previously collected, anonymized dental X-ray images of teeth that have received root canal treatment. Experienced dental specialists will evaluate these images to provide a reference standard, which will be compared with the AI system's results.

The goal of this research is to determine whether AI can provide a reliable and consistent method for evaluating root canal treatment outcomes. In the future, such technology may help dentists make more accurate decisions, improve treatment evaluation, and contribute to better patient care.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

490

Phase

  • Not Applicable

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

Ages Eligible for Study

  • Adult

Accepts Healthy Volunteers

No

Description

Periapical radiographs of teeth with completed root canal treatment from patients Aged between 18 and 60 years will be included, provided they exhibit satisfactory image quality characterized by adequate sharpness, contrast, and minimal noise, allowing clear visualization of the root canal filling and apical region. The radiographs must enable accurate assessment of obturation quality, including filling length, homogeneity, and taper. Both single-rooted and multi-rooted teeth will be considered to ensure adequate anatomical representation. Radiographs with poor image quality, significant distortion, metallic artifacts, post-core restorations, root resorption, fractures, or incomplete visualization of the apex will be excluded to ensure reliable analysis.

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

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Other: Specialist annotation
Diagnostic test: Deep learning model
This study aims to develop and evaluate an artificial intelligence (AI)-based system that can automatically assess the quality of root canal fillings using dental X-ray images. The AI system will analyze important features of the filling, including its length, uniformity, and shape, and classify the treatment quality as acceptable or needing improvement.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Evaluation of root canal obturation quality from periapical radiographs
Time Frame: 1 month
Evaluation of root canal obturation quality from periapical radiographs
1 month

Collaborators and Investigators

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

Sponsor

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)

August 1, 2026

Primary Completion (Estimated)

December 30, 2026

Study Completion (Estimated)

July 1, 2027

Study Registration Dates

First Submitted

June 29, 2026

First Submitted That Met QC Criteria

June 29, 2026

First Posted (Actual)

July 6, 2026

Study Record Updates

Last Update Posted (Actual)

July 6, 2026

Last Update Submitted That Met QC Criteria

June 29, 2026

Last Verified

June 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • New Endo 7.1.1

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.

Clinical Trials on Root Canal Treatment

Clinical Trials on Deep learning model

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Dominican Republic

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

3
Subscribe