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

AI-supported mammography screening: measuring benefit - Authors' reply.

Lancet (London, England)·2026
Same author

Artificial Intelligence Detection Scores in Screening Mammography for Early Breast Cancer Alerts.

Radiology·2026
Same author

Lesion detectability and masking disparity assessment in breast tomosynthesis across diverse populations using in-silico imaging trials.

IEEE transactions on medical imaging·2026
Same author

Cost-Effectiveness of Artificial Intelligence in Breast Cancer Screening: An Ethical Perspective on a Complex Issue.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2026
Same author

NeoCircle: pre- and post-operative circulating tumor DNA dynamics predicts survival in neoadjuvant-treated early breast cancer.

EMBO molecular medicine·2026
Same author

Breast implants in mammograph and screening-a question requiring attention.

European radiology·2026
Same journal

The bMRI-QUAL scoring system: an important first step toward standardizing breast MRI quality.

European radiology·2026
Same journal

Spectral CT-based habitat analysis for predicting pathologic response to neoadjuvant therapy in gastric cancer.

European radiology·2026
Same journal

MR-guided microwave ablation of liver tumors: outcomes in local tumor control and determinants of treatment success.

European radiology·2026
Same journal

AI integration in pediatric radiology: perspectives from international academic leaders.

European radiology·2026
Same journal

Association of hypertension and blood pressure control with aneurysm wall enhancement in unruptured intracranial aneurysms: a multicenter propensity score-matched study.

European radiology·2026
Same journal

Conservative management of < 3cm anterior mediastinal lesions in lung cancer screening is safe.

European radiology·2026
See all related articles

Related Experiment Video

Updated: Dec 10, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.4K

Identifying normal mammograms in a large screening population using artificial intelligence.

Kristina Lång1,2, Magnus Dustler3, Victor Dahlblom3,4

  • 1Diagnostic Radiology, Department of Translational Medicine, Lund University, Inga Maria Nilssons gata 47, SE-20502, Malmö, Sweden. kristina.lang@med.lu.se.

European Radiology
|September 3, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can identify normal mammograms in screening populations, potentially improving efficiency. This AI tool shows promise in reducing false positives and streamlining mammography screening processes.

Keywords:
Artificial intelligenceBreast cancerMammographyMass screening

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

627
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Related Experiment Videos

Last Updated: Dec 10, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.4K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

627
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.2K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Mammography screening is crucial for early breast cancer detection.
  • Current screening methods can lead to false positives and overdiagnosis.
  • AI offers a potential solution to enhance screening efficiency.

Purpose of the Study:

  • To evaluate the efficacy of an AI system in identifying normal mammograms within a screening population.
  • To assess the impact of AI-driven exclusion of normal mammograms on cancer detection rates and false positives.

Main Methods:

  • A retrospective analysis of 9581 mammography screening exams was conducted using a deep learning-based AI system.
  • The AI system assigned cancer risk scores to mammograms.
  • The study investigated the effect of excluding mammograms with low AI risk scores on radiologist workload and diagnostic accuracy.

Main Results:

  • Excluding mammograms with AI scores of 1 and 2 (19.1% of exams) removed false positives without missing any cancers.
  • Over half of the exams (53.0%) received low-risk scores, including 7 cancers that were mostly visible.
  • The AI system demonstrated an ability to correctly identify a significant proportion of cancer-free mammograms.

Conclusions:

  • The AI system effectively identifies normal mammograms, contributing to improved screening efficiency.
  • Excluding low-risk mammograms identified by AI can reduce the workload for radiologists and decrease false positives.
  • AI holds significant potential to optimize mammography screening programs.