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

Direct targeting of GLUT1 in cancer: A decade of inhibitor discovery and medicinal chemistry insights.

Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents·2026
Same author

Small molecule NS4B inhibitors for the treatment of the family Flaviviridae infection: A medicinal chemistry perspective.

European journal of medicinal chemistry·2026
Same author

Structure-guided modulation of the IL-4/IL-4Rα interface: molecular recognition, therapeutic antibodies, and small-molecule opportunities.

Bioorganic chemistry·2026
Same author

Polyphenols-based functional nanomaterial systems for the treatment of periodontitis.

Biomaterials advances·2026
Same author

An automated framework for quantitative alveolar bone loss using deep learning-based landmark detection.

Journal of dentistry·2026
Same author

Targeting autophagy kinases: from mechanisms to therapy with novel small molecules.

Future medicinal chemistry·2025

Related Experiment Video

Updated: Jun 11, 2026

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

Automatic detection of panoramic positioning errors and development of an objective image quality scoring system

Sijia Hu1, Erkang Tian1, Xinze Wu1

  • 1State Key Laboratory of Oral Diseases, West China School of Stomatology, National Clinical Research Center for Oral Diseases, Med-X Center for Manufacturing, Sichuan University, Chengdu, 610064, China.

Dento Maxillo Facial Radiology
|June 10, 2026
PubMed
Summary

This study developed a deep learning model to detect positioning errors in panoramic radiographs, creating an objective image quality score. The system accurately identifies errors and provides a reliable measure of perceived image quality.

Keywords:
Deep learningImage quality assessmentObjective scoringPanoramic errorsPanoramic radiography

More Related Videos

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Related Experiment Videos

Last Updated: Jun 11, 2026

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

Photorealistic Learned Landscapes for Augmented Reality
06:54

Photorealistic Learned Landscapes for Augmented Reality

Published on: June 27, 2025

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Radiology
  • Radiographic Quality Assessment

Background:

  • Panoramic radiography is crucial for dental diagnostics.
  • Image quality is often subjectively assessed, leading to variability.
  • Common positioning errors can significantly impact diagnostic accuracy.

Purpose of the Study:

  • To develop a deep learning model for automatic detection of common positioning errors in panoramic radiographs.
  • To establish an objective, error-based scoring system for panoramic image quality.
  • To align the objective scoring system with expert evaluations.

Main Methods:

  • Retrospective annotation of 4,390 panoramic radiographs for seven positioning errors by two oral radiologists.
  • Training a multi-label deep learning model (modified residual network) for simultaneous error detection.
  • Developing an objective scoring system based on multiple linear regression analysis of error contributions to subjective image quality.

Main Results:

  • High inter-rater reliability for annotations (Kappa: 0.879-0.953) and scoring (ICC: 0.838).
  • Deep learning model achieved high accuracy (88.40%-96.00%) and AUC (98.17%-99.37%) for error detection.
  • Objective score moderately correlated with expert ratings (r=0.673, p<0.001), with significant errors linked to lower subjective quality (p<0.001).

Conclusions:

  • The developed deep learning system accurately detects positioning errors in panoramic radiographs.
  • An error-based objective scoring system provides a structured and interpretable surrogate for perceived image quality.
  • This data-driven framework enhances standardization and interpretability in radiographic quality assessment.