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 Experiment Videos

Computer-aided detection for screening mammography.

Susan M Astley1

  • 1University of Manchester, Imaging Science and Biomedical Engineering, Stopford Building, Oxford Road, Manchester M13 9PT, UK. Sue.Astley@man.ac.uk

Academic Radiology
|November 9, 2004
PubMed
Summary
This summary is machine-generated.

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

Deep learning models to predict mammographic density jointly on standard dose and low dose images.

The British journal of radiology·2026
Same author

Can I Do Better Than AI: A Comparative Analysis of a Medical Student Essay Created With and Without Generative AI.

Cureus·2026
Same author

Change in mammographic density as a potential predictor of cancer recurrence after breast conservation surgery and adjuvant endocrine therapy: results of the MEDICI study.

Breast cancer research : BCR·2026
Same author

Model uncertainty estimates for deep learning mammographic density prediction using ordinal and classification approaches.

Biomedical physics & engineering express·2026
Same author

Anti-progestin therapy targets hallmarks of breast cancer risk.

Nature·2025
Same author

Mammographic density assessed using deep learning in women at high risk of developing breast cancer: the effect of weight change on density.

Biomedical physics & engineering express·2025

Computer-aided detection (CAD) systems aid mammography by highlighting potential abnormalities, aiming to reduce missed diagnoses. This review examines CAD

Area of Science:

  • Radiology
  • Medical Imaging
  • Computer-Aided Diagnosis

Background:

  • Mammographic film reading is a complex task requiring visual search for subtle abnormalities.
  • Screening mammography involves analyzing numerous images, increasing the risk of false-negative interpretations.
  • Computer-aided detection (CAD) systems are designed to assist radiologists by identifying suspicious regions.

Purpose of the Study:

  • To review the strengths and weaknesses of computer-aided detection (CAD) in screening mammography.
  • To discuss the methodologies for evaluating CAD systems in clinical settings.

Main Methods:

  • Review of existing literature on CAD systems for mammography.
  • Analysis of CAD system performance, including sensitivity and specificity of detection algorithms.

Related Experiment Videos

  • Discussion of clinical evaluation methodologies for CAD systems.

    • Main Results:

    • CAD systems can potentially reduce false-negative errors in mammography by ensuring thorough image review.
    • The effectiveness of CAD is influenced by the performance of individual detection algorithms and their interaction with human readers.
    • The impact of CAD on radiologist performance is complex and requires careful evaluation.

    Conclusions:

    • CAD systems offer potential benefits for screening mammography by improving the detection of subtle abnormalities.
    • Understanding the limitations and complexities of CAD systems is crucial for their effective implementation.
    • Further research and robust clinical evaluation are necessary to optimize the use of CAD in mammography.