Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs (Ai Retreatment)

May 20, 2026 updated by: Noha Mohamed Elsaber, Cairo University
The aim of this study is to develop and evaluate an artificial intelligence-based model capable of analyzing periapical radiographs of maxillary and mandibular molars to predict the difficulty level of non-surgical root canal retreatment. By integrating deep learning techniques with routinely acquired periapical radiographs, this study aims to enhance diagnostic support, improve clinical decision-making, and facilitate appropriate case selection or referral in endodontic practice.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Study Type

Interventional

Enrollment (Estimated)

123

Phase

  • Not Applicable

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

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

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

Periapical radiographs of maxillary and mandibular molars requiring non-surgical endodontic retreatment will be included. Radiographs should exhibit satisfactory image quality, characterized by adequate sharpness, contrast, and minimal distortion or noise to allow accurate assessment of relevant anatomical and treatment-related features. Images should clearly display the tooth of interest, surrounding periapical structures, and any existing root canal filling materials or restorations.

Exclusion Criteria:

Deciduous teeth, non-restorable, non-treated teeth

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
Experimental: Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs
This study will employ a retrospective diagnostic accuracy design focused on the development and validation of a deep learning-based model for automated prediction of endodontic retreatment difficulty in maxillary and mandibular molars using periapical radiographs. The methodology will involve radiographic data acquisition, expert annotation of case difficulty according to standardized criteria, deep learning model development and training, and comprehensive performance evaluation of the proposed system.
Diagnostic test: Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs
This study will employ a retrospective diagnostic accuracy design focused on the development and validation of a deep learning-based model for automated prediction of endodontic retreatment difficulty in maxillary and mandibular molars using periapical radiographs. The methodology will involve radiographic data acquisition, expert annotation of case difficulty according to standardized criteria, deep learning model development and training, and comprehensive performance evaluation of the proposed system.
Other Names:
  • AI model
  • Deep learning model
  • CNN model

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
diagnostic accuracy
Time Frame: From Data collection to model testing up to 60 weeks
Diagnostic performance of the deep learning model in predicting endodontic retreatment difficulty level
From Data collection to model testing up to 60 weeks

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)

July 1, 2026

Primary Completion (Estimated)

January 1, 2027

Study Completion (Estimated)

January 1, 2027

Study Registration Dates

First Submitted

May 14, 2026

First Submitted That Met QC Criteria

May 20, 2026

First Posted (Actual)

May 28, 2026

Study Record Updates

Last Update Posted (Actual)

May 28, 2026

Last Update Submitted That Met QC Criteria

May 20, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • newendo7.1.1

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.

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