AI-Based Root Canal Length, Curvature, and Morphology Assessment Using CBCT

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

Inclusion Criteria: * Pre-existing CBCT DICOM datasets of maxillary \& mandibular anterior \& premolar teeth. * Permanent teeth with fully developed roots and completely formed apices. * High-quality CBCT scans with sufficient image resolution and minimal artifacts to permit accurate visualization of the root canal system. * Teeth with intact root canal anatomy suitable for automated analysis of root canal length, curvature, number of roots, number of root canals, and canal morphology. * Teeth representing a wide range of normal anatomical variations and root canal configurations to ensure adequate diversity for AI model development and validation. * DICOM datasets suitable for image preprocessing, annotation, and deep learning analysis. Exclusion Criteria: * Teeth with incomplete root formation or open apices. * Teeth that have undergone previous endodontic treatment, retreatment, apexification, regenerative endodontic procedures, or root-end surgery. * Teeth with extensive coronal destruction, large restorations, metallic posts, intracanal filling materials, or crowns that obscure the root canal anatomy. * Teeth with root fractures, perforations, internal or external root resorption, severe root dilacerations, or other conditions that interfere with accurate anatomical assessment. * Teeth associated with extensive periapical lesions, cysts, tumors, or other pathological conditions that significantly alter the normal root canal anatomy. * Teeth with developmental anomalies affecting root morphology, including fusion, gemination, dens invaginatus, dens evaginatus, taurodontism, * CBCT datasets with severe motion artifacts, beam-hardening artifacts, metallic artifacts, excessive image noise, or poor image quality that compromises accurate image analysis. * Duplicate or incomplete DICOM datasets, corrupted image files, or datasets unsuitable for AI processing.