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

NCT07611279 | Not Yet Recruiting DMC

• 1 other identifier: newendo7.1.1
• Study Type: interventional
• Target: 123
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

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

Keywords

endodontic retreatment; difficulty assessment; endodontics; ai; artificial intelligence; deep learning model

Outcome Measures

Primary Outcomes (1)

• diagnostic accuracy

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

  From Data collection to model testing up to 60 weeks

Study Arms (1)

Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs

EXPERIMENTAL

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.

Diagnostic Test: Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs

Interventions

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.

Also known as: Deep learning model, CNN model, AI model

Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

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

Sponsors & Collaborators

• Cairo University: lead

Central Study Contacts

Noha El Saber, PhD student

CONTACT

+201157157197; nohaalsaber@dentistry.cu.edu.eg

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NA
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: May 14, 2026
• First Posted: May 28, 2026
• Study Start (Estimated): July 1, 2026
• Primary Completion (Estimated): January 1, 2027
• Study Completion (Estimated): January 1, 2027
• Last Updated: May 28, 2026

Record last verified: 2026-05