Not_yet_recruitingNCT07587060

Development and Diagnostic Accuracy of a Deep Learning Model for Root Canal Curvature Analysis in Mandibular Molars Using CBCT Scans: A Diagnostic Accuracy Study

Sponsor: Cairo University

Trial Details

Condition	Accuracy of Root Canal Curvature Analysis
Status	Not_yet_recruiting
Participants	207
Est. Completion	1 Sept 2027
Last Updated	14 May 2026

Primary Outcome

Accuracy of analysis of root canal curvature angle

Eligibility Criteria

Inclusion Criteria

Inclusion Criteria: * CBCT scans of mandibular molars of Egyptian patients aging from 18 to 65 years old * Small Field of view (FOV) including maximum a quadrant * Voxel size not larger than 2mm * Mandibular molars showing complete root formation * Carious or non-carious teeth * Absence of artifacts, dental implants in the adjacent teeth Exclusion Criteria: * Mandibular first and second molars with developmental anomalies, external or internal root resorption, root canal calcification, previous root canal treatment, post restorations, and/or root caries * CBCT images of sub-optimal quality or artifacts/high scatter interfering with proper assessment

Trial Sites

Countries

Egypt

View Registry Entry

Related Analysis

Deep Learning Model for CBCT Root Canal Analysis Enters Validation Trial — Implications for Endodontic Practice

Cairo University launches validation study for deep learning model that automatically analyses root canal curvature in CBCT scans. The technology could standardise endodontic case difficulty assessment and reduce diagnostic variability.

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