NCT07114484 (BMC-AI) is a clinical trial in Bifid Mandibular Canal, sponsored by Sara Reda Abdelhamid Aboseif.

Who is sponsoring NCT07114484?

NCT07114484 is sponsored by Sara Reda Abdelhamid Aboseif.

What condition does NCT07114484 study?

NCT07114484 studies Bifid Mandibular Canal.

Is NCT07114484 still recruiting?

NCT07114484 is currently completed. Last updated 2 January 2026.

Last reviewed 2026-01-02 · How we verify

NCT07114484: BMC-AI

Name: Drug Landscape Pharmaceutical Database
Creator: Drug Landscape
Published: 2026-01-01
License: https://creativecommons.org/licenses/by/4.0/

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

Completed Last updated 2 January 2026

What this trial tests

trial in Bifid Mandibular Canal in 117 participants. Completed in 20 October 2025.

Timeline

6 May 2025

Primary endpoint
1 October 2025

20 October 2025

Quick facts

Lead sponsor	Sara Reda Abdelhamid Aboseif
Status	Completed
Study type	OBSERVATIONAL
Enrollment	117
Start date	6 May 2025
Primary completion	1 October 2025
Estimated completion	20 October 2025
Sites	1 location across Egypt

Conditions studied

Bifid Mandibular Canal — all drugs for Bifid Mandibular Canal →

Sponsor

Sara Reda Abdelhamid Aboseif

Who can join

15 and older, any sex, with Bifid Mandibular Canal. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.

Verify or expand the search:

Verify against primary sources

ClinicalTrials.gov — authoritative US registry record
WHO ICTRP — international registry index
EU Clinical Trials Register
Sponsor press releases (Google)

Data sources for this page

Trial protocol + status: ClinicalTrials.gov NCT07114484 (US National Library of Medicine, public domain)
Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
Sponsor: as reported to ClinicalTrials.gov by Sara Reda Abdelhamid Aboseif
Last refreshed: 2 January 2026

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT07114484.

Primary sources · FDA · ClinicalTrials.gov · EMA · SEC EDGAR · ChEMBL · Wikidata · full sourcing