An interventional study of Deep Learning Model to Predict Endodontic Retreatment Difficulty from Periapical Radiographs in Endodontic Retreatment, Non-surgical Retreatment and Endodontics, sponsored by Cairo University. Not yet recruiting. Per ClinicalTrials.gov, last updated 2026-05-28.
Sponsored by Cairo University · Not applicable, Interventional, and Diagnostic
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.
Cairo University is the lead sponsor of 4,780 studies on the registry; 1,427 are open to participants now.
Of its 36 completed or terminated interventional studies of FDA-regulated products, 5 (14%) have results posted.
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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
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
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
diagnostic accuracy
Diagnostic performance of the deep learning model in predicting endodontic retreatment difficulty level
Time frame: From Data collection to model testing up to 60 weeks
No study locations are listed for this record.
Plan to share: Undecided
No publications or documents are linked to this record.
This study is not yet recruiting, as verified in May 2026. You cannot join it, but the record below documents what was studied.
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Cairo University