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

ConditionsEndodontic Retreatment Non-surgical Retreatment Endodontics AI (Artificial Intelligence)Deep Learning Model DIFFICULTY ASSESSMENT SEPARATED INSTRUMENT Perforation Missed Canals Poor Obturation Obturation Quality

Trial statusNot yet recruiting

Trial phaseNot applicable

Trial typeInterventional

Biological sexAll

AgeNot listed

SponsorCairo University

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

None

Disqualifiers

None

Trial design

Design model

Single group

Treatments tested in this trial

Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs
Diagnostic test
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.

Treatment groups

123 Participants

are divided into 1 treatment group

Group A: Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical RadiographsExperimental treatment 1 intervention

Trial outcomes

Primary outcomes

diagnostic accuracy

Diagnostic performance of the deep learning model in predicting endodontic retreatment difficulty level

Time frame

From Data collection to model testing up to 60 weeks

Locations

This trial has no locations.

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Cairo University

Lead sponsor

Source ClinicalTrials.gov

This trial is not recruiting at the moment. You can still explore other options: