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Related Concept Videos

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

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Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
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Related Experiment Video

Updated: Jun 9, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
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Artificial Intelligence-based Software for Breast Arterial Calcification Detection on Mammograms.

Alyssa T Watanabe1,2, Valerie Dib3, Junhao Wang2

  • 1Department of Radiology, University of Southern California Keck School of Medicine, Los Angeles, CA, USA.

Journal of Breast Imaging
|October 29, 2024
PubMed
Summary
This summary is machine-generated.

This study shows that artificial intelligence (AI) software accurately detects breast arterial calcifications (BACs) on mammograms. The AI tool demonstrates strong performance in identifying BACs, aiding radiologists in cardiovascular risk assessment for women.

Keywords:
artificial intelligencebreast arterial calcificationscomputer-aided detectiondeep learningmammography

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Breast arterial calcifications (BACs) are a significant cardiovascular risk marker in women.
  • Accurate detection of BACs on mammograms is crucial for risk stratification.
  • Existing methods for BAC detection may benefit from technological advancements.

Purpose of the Study:

  • To evaluate the performance of a commercial artificial intelligence (AI)-based software for detecting BACs on mammograms.
  • To compare AI performance against radiologist consensus for both digital mammography (DM) and digital breast tomosynthesis (DBT).

Main Methods:

  • Retrospective analysis of 314 DM and 277 DBT examinations from 253 and 143 patients, respectively.
  • AI-based BAC detection was assessed and compared to ground truth (GT) established by expert radiologists.
  • Key performance metrics including AUC, sensitivity, specificity, PPV, NPV, and accuracy were calculated.

Main Results:

  • AI achieved high case-level AUCs of 0.96 for DM and 0.95 for DBT.
  • Sensitivity, specificity, and accuracy ranged from 87-92% for DM and 88-90% for DBT.
  • Positive predictive value and NPV were consistently high, indicating reliable AI performance for BAC detection.

Conclusions:

  • The AI software demonstrated promising performance in detecting BACs on both DM and DBT images.
  • AI has the potential to assist radiologists in identifying and reporting BACs, a key cardiovascular risk factor.
  • This technology could enhance the clinical utility of mammography for cardiovascular risk assessment in women.