An observational study in Root Canal, sponsored by Cairo University. Not yet recruiting. Per ClinicalTrials.gov, last updated 2026-09-28.
Sponsored by Cairo University · Observational
The primary aim of this study is to develop, evaluate, and validate a deep learning-based software system capable of generating automated, comprehensive clinical reports that detect, segment, and quantify root canal curvature, total tooth length, and morphological configurations in maxillary and mandibular anterior and premolar teeth using Cone-Beam Computed Tomography (CBCT) datasets
Primary goal is to develop and validate the diagnostic performance metrics (e.g., Dice similarity coefficient, sensitivity, accuracy) of an artificial intelligence (AI)-driven tool; deep learning-based software system. That is capable of generating automated, comprehensive clinical reports based on: Automatic segmentation \& measurements of tooth length, root canal curvature, as well as segmentation and classifying morphological configurations in maxillary \& mandibular anterior \& premolar teeth using Cone-Beam Computed Tomography (CBCT) datasets.
It involves a two-phase workflow:
Pre-existing anonymized CBCT DICOM datasets of permanent maxillary and mandibular anterior and premolar teeth obtained from university hospitals and private centers. Eligible datasets will be retrospectively identified according to predefined inclusion and exclusion criteria and will be used for development, validation, and independent testing of the AI-based diagnostic system. No new imaging or intervention will be performed for the purpose of this study.
Exclusion Criteria:
Pre-existing anonymized CBCT DICOM datasets of maxillary and mandibular anterior and premolar teeth meeting the predefined eligibility criteria. The datasets will be used for development, validation, and independent testing of the deep learning-based system for automated assessment of root canal length, curvature, number of roots and canals, and root canal morphology. The independent validation set will include 155 selected teeth.
Diagnostic Test: AI-Based Deep Learning System for Root Canal Assessment
A deep learning-based diagnostic system developed to automatically analyze anonymized CBCT DICOM datasets of maxillary and mandibular anterior and premolar teeth. The system performs automated tooth and root canal segmentation and assesses root canal length, curvature, number of roots and canals, and canal morphology. The AI-generated results will be compared with an expert-derived reference standard to evaluate diagnostic accuracy and agreement. The system also generates a standardized automated clinical report for each evaluated tooth.
Tooth-level diagnostic accuracy and agreement of the AI-based system for root canal anatomical assessment
Diagnostic accuracy and agreement of the AI-based system will be assessed at the tooth level by comparing AI-generated measurements and classifications of root canal length, curvature, number of roots and canals, and canal morphology with the reference standard established by a panel of expert endodontists and oral and maxillofacial radiologists. Continuous and categorical outcomes will be evaluated using appropriate agreement and diagnostic accuracy measures.
Time frame: through study completion, an average of 1 year
No study locations are listed for this record.
Plan to share: No — Individual participant data will not be shared with other researchers because the study uses pre-existing anonymized CBCT DICOM datasets, and no plan has been established for external sharing of the individual-level data
This study is not yet recruiting, as verified in Sep 2026. You cannot join it, but the record below documents what was studied.
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Cairo University