NCT06273332

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

Assessment of the AI-assisted Registration Versus Conventional Point-based Registration on CBCTs With Heavy Metal Artifacts

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 16 (estimated)
• Sponsor: Ain Shams University · Academic / Other
• Sex: All
• Age: 15 Years
• Healthy volunteers: Accepted

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.

Detailed description

CBCT images and intraoral scans will be screened following specific eligibility criteria. 16 CBCT images and intraoral scans that will meet the inclusion criteria will undergo manual segmentation via 3D medical image processing software. Afterward, point-based registration and AI-assisted registration will be performed by a single operator using specialized implant planning software. Then, the registration accuracy will be examined by measuring the distances between the three-dimensional models of CBCT data and intraoral scans. Also, the duration required for registration will be calibrated and recorded by a stopwatch.

Conditions

• Registration Accuracy

Interventions

Type	Name	Description
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

Timeline

• Start date: 2023-12-20
• Primary completion: 2024-01-20
• Completion: 2024-02-25
• First posted: 2024-02-22
• Last updated: 2024-02-22

Locations

1 site across 1 country: Egypt