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

JADE-Plus: A Multimodal Agentic Retrieval-Augmented Generation Large Language Framework for Diagnostic Support in Jawbone Lesions: Development and Technical Validation Study.

Journal of imaging informatics in medicine·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

Three-Dimensional Globe Repositioning Following Orbital Reconstruction Independent of Bony Landmarks and Fracture Pattern Associations.

Craniomaxillofacial trauma & reconstruction·2026
Same author

Analysis of the generalizability of an artificial intelligence-based software for tomographic segmentation of posterior teeth-an external validation study.

Dento maxillo facial radiology·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

Augmented and virtual reality in dental and oral and maxillofacial surgery education: a systematic review with a taxonomy of training technologies.

BMC medical education·2026

Related Experiment Video

Updated: Jun 25, 2025

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

824

Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.

Sara Elsonbaty1,2,3, Bahaaeldeen M Elgarba1,2,4, Rocharles Cavalcante Fontenele1,2

  • 1OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium.

International Journal of Paediatric Dentistry
|May 21, 2024
PubMed
Summary

An AI platform accurately segments primary teeth on CBCT scans 35 times faster than manual methods. This automated segmentation (AS) offers expert-level precision and consistency for pediatric dental treatment planning.

Keywords:
CBCTartificial intelligencecone beam computed tomographyconvolutional neural networksdeep learningmilk teethprimary tooth

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K

Related Experiment Videos

Last Updated: Jun 25, 2025

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

824
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.4K

Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate segmentation of primary teeth on cone beam computed tomography (CBCT) scans is crucial for pediatric dental treatment planning.
  • Traditional manual segmentation methods are labor-intensive and require specialized expertise.

Purpose of the Study:

  • To validate a cloud-based artificial intelligence (AI) platform for automated segmentation (AS) of primary teeth on CBCT scans.
  • To compare the AI platform's accuracy, time efficiency, and consistency against manual segmentation (MS).

Main Methods:

  • A dataset of 402 primary teeth from 37 CBCT scans was used.
  • Manual segmentation (MS) served as the ground truth, with automated segmentation (AS) performed on the same platform.
  • Voxel- and surface-based metrics, segmentation time, and intra-class correlation coefficient (ICC) were used for comparison.

Main Results:

  • Automated segmentation achieved high accuracy (98 ± 1%) and Dice Similarity Coefficient (DSC; 95 ± 2%).
  • The AI platform was 35 times faster than manual segmentation, with an average segmentation time of 24 seconds.
  • Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively).

Conclusions:

  • The AI platform provides expert-level accuracy for primary teeth segmentation on CBCT scans.
  • The automated approach is highly time-efficient and consistent, significantly aiding pediatric dental treatment planning.