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 mammographic screening for spiculated lesions

W P Kegelmeyer1, J M Pruneda, P D Bourland

  • 1Sandia National Laboratories, Livermore, Calif.

Radiology
|May 1, 1994
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

The SIESTA method; developments and applicability.

Journal of physics. Condensed matter : an Institute of Physics journal·2011
Same author

Unintended changes in cognition, mood, and behavior arising from cell-based interventions for neurological conditions: ethical challenges.

The American journal of bioethics : AJOB·2009
Same author

eScience for molecular-scale simulations and the eMinerals project.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2008
Same author

Cell-based interventions for neurologic conditions: ethical challenges for early human trials.

Neurology·2008
Same author

Ferrodistortive instability at the (001) surface of half-metallic manganites.

Physical review letters·2008
Same author

Electronic stopping power in LiF from first principles.

Physical review letters·2008

Computer vision significantly improves mammogram screening. This AI tool enhanced radiologist detection of spiculated lesions, increasing cancer diagnosis accuracy without reducing specificity.

Area of Science:

  • Radiology
  • Medical Imaging
  • Computer Vision

Background:

  • Mammography is a key tool for breast cancer screening.
  • Early detection of spiculated lesions is crucial for effective treatment.
  • Radiologist performance can be enhanced with decision support tools.

Purpose of the Study:

  • To evaluate a computer vision algorithm as a second reader for detecting spiculated lesions on screening mammograms.
  • To assess the impact of computer-aided detection on radiologist performance.

Main Methods:

  • An algorithmic computer process was developed for detecting spiculated lesions on digitized mammograms.
  • The algorithm was tested on 85 clinical cases (36 with cancer, 49 negative).
  • Four radiologists screened cases twice: once independently and once with computer-generated reports.

Related Experiment Videos

Main Results:

  • The computer vision algorithm alone demonstrated 100% sensitivity and 82% specificity.
  • Computer reports improved average radiologist sensitivity by 9.7% (from 80.6% to 90.3%).
  • This improvement in sensitivity was statistically significant (P = .005) with no reduction in specificity.

Conclusions:

  • Computer analysis of mammograms can substantially enhance radiologist screening efficacy.
  • AI-powered second readers offer a promising approach to improve diagnostic accuracy in mammography.
  • This technology has the potential to improve patient outcomes through earlier and more accurate cancer detection.