Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Computed Tomography01:10

Computed Tomography

9.5K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
9.5K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

633
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
633

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Maxillary Anterior Horizontal Ridge Reconstruction using Computer-Guided Ridge Split and Expansion: A Randomized Clinical Trial with a 2-Year Follow-up.

Journal of dentistry·2026
Same author

Clinical Applicability of Artificial Intelligence-Driven Implant Planning and Surgical Guide Design in the Maxillary Esthetic Zone: A Registry-Based Cohort Study.

Clinical oral implants research·2026
Same author

Advances in artificial intelligence enhanced robotics for dental implant placement: A scoping review.

Journal of dentistry·2026
Same author

Comparative Analysis of Artifact Expression in Zirconia and Graphene Crowns in CBCT Images From Different Systems.

International journal of dentistry·2026
Same author

Automating virtual dental implant planning: can artificial intelligence match clinical expertise?

Dento maxillo facial radiology·2026
Same author

AI-driven gingival segmentation on CBCT: Validation using delineation by intraoral scanning and CBCT-based cotton roll separation.

Journal of dentistry·2026

Related Experiment Video

Updated: Mar 27, 2026

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.6K

Artificial intelligence guided occlusion reconstruction in nonoccluding CBCT: A validation study.

Eslam Abdelwahab Dawood1, Bahaaeldeen M Elgarba2, Rocharles Cavalcante Fontenele3

  • 1PhD Researcher, OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven; Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium; and Assistant Lecturer, Department of Prosthodontics, Faculty of Dentistry, Tanta University, Tanta, Egypt.

The Journal of Prosthetic Dentistry
|March 25, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) accurately reconstructs dental occlusion from cone beam computed tomography (CBCT) scans with interocclusal separation by integrating intraoral scans (IOS). This AI-driven fusion enables reliable treatment planning without requiring CBCT scans in occlusion.

More Related Videos

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.4K
Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.9K

Related Experiment Videos

Last Updated: Mar 27, 2026

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.6K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

2.4K
Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.9K

Area of Science:

  • Dental diagnostics
  • Medical imaging analysis
  • Artificial intelligence in dentistry

Background:

  • Cone beam computed tomography (CBCT) with interocclusal separation reduces artifacts but lacks occlusal data.
  • Accurate occlusion reconstruction is vital for dental diagnosis, restorative planning, and implant procedures.
  • Integrating intraoral scans (IOS) offers precise occlusal data, but AI's role in reconstructing separated CBCT occlusion is unproven.

Purpose of the Study:

  • To validate an AI tool for reconstructing occlusion.
  • The AI tool aligns separated CBCT scans using IOS-derived occlusal data.
  • IOS occlusion served as the reference standard for validation.

Main Methods:

  • Forty paired CBCT scans and IOS datasets were analyzed using an AI platform.
  • The AI tool segmented and registered CBCT and IOS data, aligning CBCT models with IOS occlusion.
  • Occlusal contacts, intersections, contact area, and 3D deviations were quantified; statistical analyses were performed.

Main Results:

  • AI-fused CBCT-IOS models showed no significant difference from IOS models in occlusal contacts or contact area.
  • CBCT-only models exhibited significantly fewer occlusal contacts.
  • Median surface deviation between IOS and AI-fused models was 0 µm, compared to 70-80 µm for CBCT-only models.

Conclusions:

  • AI-driven fusion of IOS data with separated CBCT scans accurately reconstructs occlusion.
  • This method provides reliable occlusal data for treatment planning.
  • CBCT acquisition in occlusion is unnecessary when using this AI fusion technique.