Development and Validation of a Deep Learning Model to Predict Endodontic Retreatment Difficulty From Periapical Radiographs
Sponsor: Cairo University
Trial Details
| Condition | Endodontic Retreatment, Non-surgical Retreatment, Endodontics, AI (Artificial Intelligence), Deep Learning Model, DIFFICULTY ASSESSMENT, SEPARATED INSTRUMENT, Perforation, Missed Canals, Poor Obturation, Obturation Quality |
| Intervention | Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs |
| Phase | NA |
| Status | Not_yet_recruiting |
| Participants | 123 |
| Est. Completion | 1 Jan 2027 |
| Last Updated | 28 May 2026 |
Primary Outcome
diagnostic accuracy
Eligibility Criteria
Inclusion Criteria
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
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