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

Using Citric-Acid-Based Anodization to Form Magnesium-Doped Carbonated Apatite-Containing Oxides on Solid and 3D-Printed Titanium Substrates.

Journal of functional biomaterials·2026
Same author

Deep learning-based segmentation of caries, implants, fixed prosthesis, and restorations on bitewing radiographs: A retrospective study.

Science progress·2026
Same author

Organic Acid-Based Anodization Process to Produce Bioactive Oxides on Titanium Implants.

Materials (Basel, Switzerland)·2025
Same author

Is high-power laser irradiation a time efficient method to debond zirconia restorations?

Dental materials : official publication of the Academy of Dental Materials·2025
Same author

Biomechanical assessment of zygomatic implants in clinical rehabilitation scenarios: A finite element and fatigue analysis.

Dental materials : official publication of the Academy of Dental Materials·2025
Same author

Citrus-Fruit-Based Hydroxyapatite Anodization Coatings on Titanium Implants.

Materials (Basel, Switzerland)·2025
Same journal

Gene expression in visceral adipocytes in metabolically healthy and unhealthy obesity: A cross-sectional analysis of associations with cardiometabolic components.

Science progress·2026
Same journal

Proteomic profiling reveals mitochondrial metabolic alterations in dexamethasone-induced neuronal differentiation.

Science progress·2026
Same journal

Stroke risk associated with the interaction between composite dietary antioxidant index and heavy metals: A cross-sectional explainable machine learning study using NHANES data.

Science progress·2026
Same journal

Neuroimaging in schizophrenia: From group-average abnormalities to individualised circuit models.

Science progress·2026
Same journal

Clinical and mechanistic effects of GLP-1 receptor agonists in hidradenitis suppurativa and comorbidities.

Science progress·2026
Same journal

Association between serum albumin-to-globulin ratio and diabetic retinopathy: A cross-sectional study based on the 2001-2020 NHANES database.

Science progress·2026
See all related articles

Related Experiment Video

Updated: Jun 10, 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

780

Automatic feature segmentation in dental panoramic radiographs.

Rohan Jagtap1, Yalamanchili Samata2, Amisha Parekh3

  • 1Division of Oral & Maxillofacial Radiology, Department of Care Planning & Restorative Sciences, University of Mississippi Medical Center School of Dentistry, Jackson, MS, USA.

Science Progress
|October 17, 2024
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) system demonstrated strong diagnostic performance in detecting dental issues like caries and implants on panoramic radiographs, comparable to human radiologists. This AI tool shows promise for improving accuracy and efficiency in dental diagnostics.

Keywords:
Artificial intelligencecariesdental restorationdentistrydiagnosisfixed prosthesisimplantspanoramic radiographsteeth numbering

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

1.7K
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

4.4K

Related Experiment Videos

Last Updated: Jun 10, 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

780
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

1.7K
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

4.4K

Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Radiology

Background:

  • Panoramic radiography is crucial for dental diagnosis and treatment planning.
  • Image quality issues like artifacts and superimpositions can lead to misinterpretations.
  • Automated detection systems are needed to overcome these diagnostic challenges.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of an AI system for identifying teeth, caries, implants, restorations, and fixed prostheses on panoramic radiographs.
  • To compare the AI system's performance against human expert annotations.

Main Methods:

  • A cross-sectional study analyzing 1000 panoramic radiographs from 500 adult patients.
  • An AI system automatically detected dental features.
  • AI findings were compared with annotations from two oral and maxillofacial radiologists.

Main Results:

  • The AI system showed strong correlations (R > 0.5) with radiologists' assessments.
  • High accuracy was observed for detecting implants (0.770-0.952), restored teeth (0.773-0.834), and fixed prostheses (0.972-0.980).
  • The AI also performed well in identifying carious (0.691-0.878) and missing teeth (0.956-0.988).

Conclusions:

  • The AI system's automatic detection performance is comparable to that of oral radiologists.
  • AI may assist in automatic identification tasks on panoramic radiographs.
  • AI systems have the potential to enhance diagnostic accuracy and efficiency in dentistry.