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

[Expression of S100P in Lung Adenocarcinoma and Its Clinical Significance].

Zhongguo fei ai za zhi = Chinese journal of lung cancer·2026
Same author

Neutrophil-Mediated Inflammatory Response to Zinc Bone Implants: A Single-Cell Transcriptomic Landscape.

Advanced healthcare materials·2026
Same author

Integrin-β1/FAK signaling is involved in electrical stimulation to prevent disuse muscular atrophy induced by tail suspension in mice.

BMC musculoskeletal disorders·2026
Same author

TBL1XR1 mutations promoted tumor progression in diffuse large B-cell lymphoma through impairing nature killer cytotoxicity via the MYC-CD47/PD-L1 axis.

Molecular cancer·2026
Same author

Large-area two-dimensional MoO<sub>3</sub> as a high-κ dielectric for van der Waals integration.

Nature communications·2026
Same author

Robotic-assisted versus laparoscopic esophageal hiatal hernia and anti-reflux surgery: A comprehensive systematic review and meta-analysis.

Hernia : the journal of hernias and abdominal wall surgery·2026

Related Experiment Video

Updated: Sep 14, 2025

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

1.9K

Automatic Point Cloud Patching of Intraoral Three-Dimensional Scanning Based on Deep Learning.

Qianhan Zheng1, Yimin Wang2, Mengqi Zhou1

  • 1Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China.

International Dental Journal
|July 20, 2025
PubMed
Summary

This study introduces a deep learning method to automatically restore missing data in intraoral scans (IOS). The AI model accurately reconstructs incomplete 3D point clouds, enhancing digital dentistry workflows.

Keywords:
CompletionDeep learningIntraoral scanPoint cloud

More Related Videos

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

999
Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities
07:14

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

4.6K

Related Experiment Videos

Last Updated: Sep 14, 2025

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

1.9K
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

999
Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities
07:14

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

4.6K

Area of Science:

  • Digital dentistry
  • Artificial intelligence in healthcare
  • 3D imaging and reconstruction

Background:

  • Intraoral scanning (IOS) is crucial for digital dentistry but often suffers from data loss due to the complex oral environment.
  • Incomplete 3D point clouds from IOS hinder the accuracy and efficiency of digital orthodontic workflows.

Purpose of the Study:

  • To develop and evaluate a deep learning-based method for automatic restoration of missing regions in intraoral 3D point clouds.
  • To improve the accuracy and efficiency of digital orthodontic workflows by addressing data loss in IOS.

Main Methods:

  • A Point Fractal Network architecture was utilized for reconstructing incomplete IOS data.
  • A dataset of 314 IOS scans (4162 teeth) was used, with simulated data loss (5-20%) for training and validation.
  • Model performance was evaluated using Chamfer distance (CD) to quantify point cloud completion accuracy.

Main Results:

  • The deep learning model demonstrated robust performance, achieving average CD values below 0.01 across various data loss levels.
  • Visual assessment confirmed high geometric fidelity between completed and original 3D point clouds.
  • The model processed each point cloud in approximately 0.5 seconds, enabling near real-time restoration.

Conclusions:

  • The developed deep learning model accurately restores missing IOS data, significantly enhancing the precision and efficiency of digital dental workflows.
  • The method's speed and accuracy support real-time clinical applications, reducing manual corrections and improving treatment outcomes.
  • This AI-driven approach has the potential to minimize human error, increase dental restoration precision, and facilitate broader AI integration in clinical practice.