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

NCT07611279 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 123

Last updated 2026-05-28

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs

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.

Sponsors & Collaborators

  • Cairo University

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-07-31
Primary Completion
2027-01-31
Completion
2027-01-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07611279 on ClinicalTrials.gov