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

Starting soon · Not applicable

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

In brief

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.

Key facts

Study ID

NCT07611279

Run by

Cairo University

People needed

123

Starts

2026-07-01

Expected to finish

2027-01-01

Last updated by the study team

2026-05-28

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• 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.

You may not qualify if…

• Deciduous teeth, non-restorable, non-treated teeth

Full record on ClinicalTrials.gov

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