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.

NCT Number	NCT07611279
Lead Sponsor	Cairo University
Conditions	Endodontic Retreatment, Non-surgical Retreatment, Endodontics, AI (Artificial Intelligence), Deep Learning Model, DIFFICULTY ASSESSMENT, SEPARATED INSTRUMENT, Perforation +3 more
Enrollment	123 participants
Start Date	2026-07
Primary Completion	2027-01 (estimated)
Study Completion	2027-01 (estimated)
Updated on ClinicalTrials.gov	2026-05-28