Not yet recruiting NCT07611279

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

No phase Interventional Endodontic Retreatment Non-surgical Retreatment Endodontics AI (Artificial Intelligence)

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied: The protocol lists: Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs.
Who it may be relevant to: Registry conditions: Endodontic Retreatment, Non-surgical Retreatment, Endodontics, AI (Artificial Intelligence). Basic parameters: No limits · All.
What needs checking: Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place: Center list to be confirmed — check the primary protocol.
Next step: Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Overview

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 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 propose

Primary outcome measures

diagnostic accuracy [Time frame: From Data collection to model testing up to 60 weeks]

Eligibility 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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation: N/A
Model: Single group
Masking: Open label
Primary purpose: Diagnostic

Study locations

Center list to be confirmed — check the primary protocol.

Identifiers

NCT: NCT07611279 · newendo7.1.1

Primary sources (government registries)

ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗