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

Status

Not yet recruiting

Phases

Unknown

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT07611279

Acronym

Ai Retreatment

Enrollment

123

Registered

2026-05-28

Start date

2026-07-01

Completion date

2027-01-01

Last updated

2026-05-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

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

Keywords

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

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.

Interventions

DIAGNOSTIC_TESTDeep 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.

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Eligibility

Sex/Gender

ALL

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
diagnostic accuracy	From Data collection to model testing up to 60 weeks	Diagnostic performance of the deep learning model in predicting endodontic retreatment difficulty level

Contacts

CONTACTNoha El Saber, PhD student

nohaalsaber@dentistry.cu.edu.eg+201157157197

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: May 29, 2026