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

NCT ID: NCT06273332

Last Updated: 2024-02-22

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 16 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-12-20
• Study Completion Date: 2024-02-25

Brief 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

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: OTHER
• Blinding Strategy: NONE

Study Groups

AI-assisted registration

3D model registration will be carried out using artificial intelligence

Group Type: EXPERIMENTAL

AI-assisted registration

Intervention Type: OTHER

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

Point-based registration

3D model registration will be carried out using point-based approach

Group Type: ACTIVE_COMPARATOR

Point-based registration

Intervention Type: OTHER

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

Interventions

AI-assisted registration

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

Intervention Type: OTHER

Point-based registration

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

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

CBCT scans for either the maxilla or mandible or both and intraoral scans or manual impressions with metal restorations.

Exclusion Criteria

Scans without metal restorations.

• Minimum Eligible Age: 15 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Ain Shams University

OTHER

Sponsor Role: lead

Responsible Party

Nehal Ibrahim Ahmed Shobair

principal investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Private maxillofacial digital lab

Cairo, , Egypt

Countries

Egypt

Other Identifiers

AI surgery protocol

Identifier Type: -

Identifier Source: org_study_id