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

U-Net-Based Deep Learning for Simultaneous Segmentation and Agenesis Detection of Primary and Permanent Teeth in Panoramic Radiographs.

Diagnostics (Basel, Switzerland)·2025
Same author

Comparison of nasal passage and palatal volume in obstructive sleep apnea patients and healthy individuals using cone beam computed tomography.

Sleep & breathing = Schlaf & Atmung·2025
Same author

Evaluation of nasal passage and palatal morphology in patients with impacted maxillary canine: a retrospective study using cone-beam computed tomography.

BMC oral health·2025
Same author

U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs.

Oral radiology·2025
Same author

Deep Learning-Based Detection of Separated Root Canal Instruments in Panoramic Radiographs Using a U<sup>2</sup>-Net Architecture.

Diagnostics (Basel, Switzerland)·2025
Same author

Segmentation of Pulp and Pulp Stones with Automatic Deep Learning in Panoramic Radiographs: An Artificial Intelligence Study.

Dentistry journal·2025

Related Experiment Video

Updated: Feb 28, 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

Evaluation of Root Angulations Through Panoramic Films Using Artificial Intelligence.

Deniz Şevik1, Nurullah Akkaya2, Ulas Oz3

  • 1Department of Orthodontics, Faculty of Dentistry, Near East University, 99138 Mersin, Turkey.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

An artificial intelligence (AI) algorithm accurately measures tooth root angulation on panoramic radiographs, offering objective and reproducible results for orthodontic assessment. This AI tool enhances clinical decision-making by reducing observer variability in evaluating root parallelism.

Keywords:
artificial intelligenceautomated image analysisconvolutional neural networksdeep learningorthodonticspanoramic radiographyroot angulationroot parallelismtooth segmentation

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
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

5.3K

Related Experiment Videos

Last Updated: Feb 28, 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
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

5.3K

Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate root angulation assessment is crucial for orthodontic treatment outcomes.
  • Current visual inspection of panoramic radiographs is subjective and variable.
  • Objective, quantitative methods are needed for reliable root angulation evaluation.

Purpose of the Study:

  • To develop and validate an AI algorithm for automated mesiodistal root angulation assessment.
  • To compare the AI algorithm's accuracy against manual measurements.
  • To evaluate the AI's potential to improve orthodontic diagnostics.

Main Methods:

  • Developed a U2-Net deep learning model for automatic tooth segmentation.
  • Calculated tooth long-axis orientation using principal component analysis.
  • Validated the AI algorithm against manual measurements by experienced examiners on 214 panoramic radiographs.

Main Results:

  • Manual measurements showed high intra- and inter-examiner reliability (ICC > 0.96).
  • Excellent agreement was found between the AI algorithm and manual measurements (ICC = 0.941).
  • Bland-Altman analysis indicated minimal bias and no proportional error in AI measurements.

Conclusions:

  • The AI-based algorithm provides accurate, objective, and reproducible root angulation measurements.
  • This AI tool can aid clinical decision-making in orthodontics.
  • The algorithm has the potential to reduce observer variability and streamline root parallelism assessment.