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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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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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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Noha El Saber, PhD student | Contact | +201157157197 | nohaalsaber@dentistry.cu.edu.eg |
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