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

Clinical and computed tomography outcome after primary and revisional fundoplication.

Surgical endoscopy·2026
Same author

Multi-institutional classification of fibroglandular tissue and background parenchymal enhancement in breast MRI using deep learning.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

Copeptin kinetics in healthy aging.

The Journal of clinical endocrinology and metabolism·2026
Same author

Whole-Body Transformation in Obese Patients Undergoing Metabolic Surgery: Insights From Automated Multiorgan Segmentation.

Academic radiology·2026
Same author

Effect of oral urea ingestion on growth hormone levels in healthy adults - a secondary analysis of a randomized, double-blind, placebo-controlled cross-over trial.

Pituitary·2025
Same author

Case Report: Metastatic colorectal cancer with ALK-CEP44 fusion and rapid resistance development.

Frontiers in oncology·2025
Same journal

Body composition's effect on the bone-vascular axis of osteoporosis discovered in AI-based CT analysis of COPD patients.

European radiology·2026
Same journal

ESR Essentials: pelvic floor imaging-practice recommendations by the European Society of Urogenital Radiology.

European radiology·2026
Same journal

STIR or T2-Dixon? A false dilemma in musculoskeletal MRI.

European radiology·2026
Same journal

ESR Essentials: uterine cancers-practice recommendations by the European Society of Urogenital Radiology.

European radiology·2026
Same journal

Adjunctive quantification for more reproducible amyloid PET interpretation.

European radiology·2026
Same journal

APEX-NET: automated pancreatic evaluation network using early non-contrast CT.

European radiology·2026
See all related articles

Related Experiment Video

Updated: Sep 15, 2025

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.7K

Enhancing breast positioning quality through real-time AI feedback.

Raphael Sexauer1,2, Friederike Riehle3, Karol Borkowski4

  • 1Department of Radiology and Nuclear Medicine, Kantonsspital Baselland, Liestal, Switzerland. raphael.sexauer@ksbl.ch.

European Radiology
|July 15, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) software significantly improved mammography quality by reducing inadequate images from 13.31% to 3.20%. This AI-driven feedback enhances cancer detection and supports better breast cancer screening outcomes.

Keywords:
Breast neoplasmsDeep learningFeedbackMammographyQuality improvement

More Related Videos

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

853
Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization
06:50

Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization

Published on: March 3, 2023

1.6K

Related Experiment Videos

Last Updated: Sep 15, 2025

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.7K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

853
Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization
06:50

Troubleshooting FoCUS Image Acquisition: Patient Positioning, Transducer Manipulation, and Image Optimization

Published on: March 3, 2023

1.6K

Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Breast Cancer Screening

Background:

  • Mammography quality is crucial for accurate cancer detection and reducing interval cancers.
  • Inadequate image quality can negatively impact screening sensitivity.
  • Continuous quality assessment is essential for maintaining high standards in mammography.

Purpose of the Study:

  • To evaluate the impact of AI-driven feedback using 'b-boxTM' software on mammography quality.
  • To assess improvements in image quality based on the 'Perfect', 'Good', 'Moderate', and 'Inadequate' (PGMI) criteria.
  • To determine if AI implementation can reduce the rate of inadequate mammograms.

Main Methods:

  • Comparative analysis of PGMI scores before and after AI software implementation.
  • Evaluation of mammograms from a tertiary hospital, including screening and diagnostic cases.
  • Assessment of image quality by two readers over multiple time points (pre-implementation, 2021, 2022, 2023).

Main Results:

  • A significant improvement in diagnostic image quality was observed (p < 0.01).
  • The percentage of 'Perfect' examinations increased from 22.34% to 32.27%.
  • The rate of 'Inadequate' mammograms decreased from 13.31% to 5.41% in 2021, further reducing to 3.20% by 2023.

Conclusions:

  • AI-driven quality evaluation software can lead to lasting improvements in mammography image quality.
  • Real-time AI feedback supports radiographers' professional development and enhances institutional standards.
  • Implementing AI tools in mammography screening improves diagnostic reliability and contributes to better patient outcomes.