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Accuracy of AI in Detecting Bifid Mandibular Canal on CBCT: A Diagnostic Accuracy Study

NCT07114484 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 117

Last updated 2026-01-02

No results posted yet for this study

Summary

The goal of this observational study is to evaluate how accurately a deep learning-based artificial intelligence (AI) model can detect and segment bifid mandibular canals (BMCs) on cone-beam computed tomography (CBCT) scans in Egyptian patients. This condition is a key anatomical variation that, if missed, may cause surgical complications such as nerve injury.

The study uses previously collected CBCT scans of individuals aged 15 and older from the Oral and Maxillofacial Radiology Department at Cairo University. The scans will be analyzed retrospectively.

The main questions it aims to answer are:

How closely does the AI model's segmentation of the mandibular canal match the expert manual segmentation?

How accurate is the AI model in identifying the presence or absence of bifid mandibular canals?

Participants are not actively involved. Instead, anonymized CBCT data will be analyzed using the AI model and compared to expert annotations to measure diagnostic performance.

Conditions

  • Bifid Mandibular Canal

Sponsors & Collaborators

  • Cairo University

    collaborator OTHER

  • Sara Reda Abdelhamid Aboseif

    lead OTHER

Principal Investigators

  • Enas Anter, Ph.D · Cairo University

Eligibility

Min Age
15 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-05-06
Primary Completion
2025-10-01
Completion
2025-10-20

Countries

  • Egypt

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07114484 on ClinicalTrials.gov