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

Generating Findings for Jaw Cysts in Dental Panoramic Radiographs Using a GPT-Based VLM: A Preliminary Study on Building a Two-Stage Self-Correction Loop with a Structured Output (SLSO) Framework.

Diagnostics (Basel, Switzerland)·2026
Same author

Impact of image processing techniques on deep learning-based classification accuracy of cervical vertebral maturation.

Oral radiology·2026
Same author

A Logistic Regression Model for Predicting Osteoporosis Using Alveolar Bone Mineral Density Measured on Intraoral Radiographs Combined with Panoramic Mandibular Cortical Index.

Journal of clinical medicine·2025
Same author

Impacts of X-ray beam angulation and image receptor positioning on the quantification of alveolar bone mineral density using intraoral radiographic technique.

Oral radiology·2025
Same author

Detection and dual-label classification of tooth number and condition in dental panoramic radiographs including deciduous teeth.

Radiological physics and technology·2025
Same author

Evolutionary Implications of the Human Soleus Muscle Based on the Comparative Anatomy of Detailed Intramuscular Nerve Distribution Patterns in Primates.

Journal of morphology·2025

Related Experiment Video

Updated: Jul 14, 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

879

Deep learning and artificial intelligence in dental diagnostic imaging.

Akitoshi Katsumata1

  • 1Department of Oral Radiology, Asahi University School of Dentistry, Japan.

The Japanese Dental Science Review
|October 9, 2023
PubMed
Summary

Artificial intelligence (AI) using deep learning is advancing dental diagnostic imaging. AI tools can now classify abnormalities, identify teeth, and even generate reports from dental images.

Keywords:
ClassificationDeep learningDental imagingPanoramic radiographRegion detectionSegmentation

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

1.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Related Experiment Videos

Last Updated: Jul 14, 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

879
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

1.8K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K

Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Deep learning applications in dental diagnostic imaging are rapidly expanding.
  • Various deep learning tasks are being adapted for analyzing dental images.

Purpose of the Study:

  • To explore the diverse applications of deep learning in dental diagnostic imaging.
  • To highlight the capabilities of AI in tasks such as classification, detection, segmentation, and report generation.

Main Methods:

  • Utilizing classification tasks for identifying abnormal findings and lesion progression.
  • Employing region detection and segmentation for tooth identification and dental charting.
  • Leveraging deep learning for anatomical structure analysis.
  • Implementing generative AI with natural language processing for automated report creation.

Main Results:

  • Deep learning effectively classifies dental images for diagnostic purposes.
  • AI enables automated tooth identification and dental chart creation from radiographs.
  • AI assists in detecting and evaluating anatomical structures.
  • Generative AI can automatically produce written diagnostic imaging reports.

Conclusions:

  • Deep learning offers a powerful suite of tools for enhancing dental diagnostics.
  • AI applications streamline workflows, improve accuracy, and aid in clinical decision-making.
  • The integration of AI promises to revolutionize dental imaging analysis and reporting.