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

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.

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Key information

Conditions

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

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Other names: Deep learning model, CNN model, AI model

Primary outcomes

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

Study contacts

Contact information is provided by the study sponsor or research team.

Noha El Saber, PhD student

CONTACT

[email protected]

+201157157197

Sponsors and collaborators

Lead sponsor

Cairo University

Other

Registry information

Acronym: Ai Retreatment

Important dates

Study start: 2026
Primary completion: 2027
Study completion: 2027
First posted: May 28, 2026
Registry last updated: May 28, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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Completed