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Assessment of the Artifical Intelligence Assisted Registration Versus Conventional Point Based Registration on Cone Beam-computed Tomography (CBCT) With Heavy Metal Artifacts

NCT06273332 · Status: ACTIVE_NOT_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 16

Last updated 2024-02-22

No results posted yet for this study

Summary

Our study investigates the accuracy and duration needed for 3D model registration using artifical intelligence (AI) assistance compared to conventional point-based registration. Manual segmentation of all cone beam computed tomography (CBCT) scans will be performed before the registration procedure.

Conditions

  • Registration Accuracy

Interventions

OTHER

AI-assisted registration

We will use artificial intelligence to register 3d model on intra-oral scan

OTHER

Point-based registration

We will use five references points or more to perform model registration

Sponsors & Collaborators

  • Ain Shams University

    lead OTHER

Study Design

Allocation
NON_RANDOMIZED
Purpose
OTHER
Masking
NONE
Model
PARALLEL

Eligibility

Min Age
15 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-12-20
Primary Completion
2024-01-20
Completion
2024-02-25

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 NCT06273332 on ClinicalTrials.gov