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

You might also read

Related Articles

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

Sort by
Same author

How manure amendment counters no-tillage yield reduction in winter wheat: enhanced nitrogen components and soil enzyme activity.

Frontiers in plant science·2026
Same author

Association between skeletal muscle index and kidney stones in health screening populations: a single-center cross-sectional study.

Translational andrology and urology·2026
Same author

Surface-hydrogenation activity regulation toward robust anti-poisoning of ZrCo-based hydrogen isotope storage materials.

Chemical science·2026
Same author

Sec31a and its impact on ER stress and Bmp/Smad signaling in senescent BMSCs.

Tissue & cell·2026
Same author

Surface modification of chitin nanocrystals with hydroxyapatite deposition and polymer grafting for poly(lactic acid)-based osteogenic biomaterials.

International journal of biological macromolecules·2026
Same author

ADAMTS7 selective inhibitor BAY-9835 alleviates acute myocardial infarction by suppressing the NF-κB-mediated pyroptosis.

Toxicology and applied pharmacology·2026
Same journal

Refusal of Hospital-Based Oral Cancer Treatment Driven by Health Beliefs in Pakistan: A Qualitative Study.

International dental journal·2026
Same journal

Pulpal Pressure Aggravates Pulpitis by Mechano-Inflammatory Signal Synergy.

International dental journal·2026
Same journal

Global Burden of Lip and Oral Cavity Cancer Attributed to Different Tobacco Use From 1990 to 2021 and Projection to 2050.

International dental journal·2026
Same journal

Decoding the Dynamic Landscape of Sequential Disease Stages from Oral Submucous Fibrosis to Oral Squamous Cell Carcinoma.

International dental journal·2026
Same journal

Comment on "Complete-Arch Implant Impression Precision: Digital vs Conventional Techniques".

International dental journal·2026
Same journal

Multiomics Reveals NDRG1-Driven Oral Squamous Cell Carcinoma Progression: Competitive Endogenous RNAs Regulation and Proangiogenic Microenvironment Remodelling.

International dental journal·2026
See all related articles

Related Experiment Video

Updated: Mar 20, 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

Automated Tooth Detection and Caries Identification in CBCT With Deep Learning.

Surong Chen1, Weiwei Wu1, Pan Chen1

  • 1Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

International Dental Journal
|March 18, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a two-stage deep learning framework for automated tooth detection, numbering, and caries identification in cone-beam computed tomography (CBCT) images. The framework shows promise for opportunistic caries screening and prioritizing clinician review of CBCT scans.

Keywords:
Caries identificationCone-beam computed tomographyDeep learningTooth detection

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 20, 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:

  • Artificial Intelligence in Dentistry
  • Medical Imaging Analysis
  • Deep Learning for Dental Diagnostics

Background:

  • Automated localization and numbering of carious teeth in CBCT images is underexplored.
  • Deep learning shows potential for caries diagnosis but requires robust tooth localization.

Purpose of the Study:

  • To develop a two-stage deep learning framework for tooth detection, numbering, and caries identification in CBCT images.
  • To provide a technical basis for automated analysis supporting opportunistic caries screening.

Main Methods:

  • Retrospective study using CBCT images from 65 patients.
  • YOLOv3 and Cascade R-CNN were compared for tooth detection.
  • DenseNet169, MobileNet_V2, and ResNet50 were evaluated for caries identification.

Main Results:

  • YOLOv3 demonstrated superior tooth detection performance (P < .0001).
  • DenseNet169 achieved the best caries identification with balanced accuracy of 0.7414 and MCC of 0.6074.
  • The integrated framework showed acceptable overall performance.

Conclusions:

  • The proposed two-stage framework shows promising performance for detecting, numbering, and identifying caries in CBCT images.
  • This supports the feasibility of opportunistic screening using CBCT scans for non-caries indications.
  • Clinical utility requires further multi-center and prospective validation.