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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: Jan 12, 2026

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Deep learning-based automated quantification system for abdominal aortic calcification: multicenter cohort study for

Zhenhong Shao1,2, Enhui Xin3, Lisong Chen1

  • 1Department of Radiology, Cixi People's Hospital Medical Health Group (Cixi People's Hospital), Ningbo, Zhejiang, China.

Frontiers in Cardiovascular Medicine
|November 6, 2025
PubMed
Summary

An automated system for abdominal aortic calcification (AAC) scoring was developed, showing high accuracy and reliability. This tool aids in standardized imaging analysis for better atherosclerosis management and cardiovascular risk stratification.

Keywords:
abdominal aortic calcificationautomated quantificationcardiovascular riskstratificationdeep learningx-ray image

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Cardiovascular Disease Research

Background:

  • Abdominal aortic calcification (AAC) is a significant indicator of atherosclerosis.
  • Standardized quantitative imaging analysis is crucial for clinical decision-making in atherosclerosis management.
  • Manual scoring of AAC can be subjective and time-consuming.

Purpose of the Study:

  • To develop and validate an automated scoring system for abdominal aortic calcification (AAC).
  • To facilitate standardized quantitative imaging analysis for atherosclerosis management.
  • To improve cardiovascular risk stratification in clinical practice.

Main Methods:

  • Utilized x-ray images from 2,941 individuals across five medical centers.
  • Developed a two-component automated framework: lumbar spine segmentation (nnUnet) and AAC score regression (ResNet).
  • Validated the model using 1,737 training cases, 471 internal validation cases, and 733 external validation cases.

Main Results:

  • The automated system achieved high accuracy with low mean absolute errors (1.686 internal, 1.920 external).
  • Demonstrated strong correlation with expert ratings (Spearman's ρ = 0.923 internal, 0.888 external).
  • Showed excellent inter-rater reliability (ICC = 0.913 internal, 0.874 external) and high sensitivity/specificity across calcification categories.

Conclusions:

  • The developed automated AAC quantification system is efficient and accurate.
  • Offers a standardized approach for quantitative imaging analysis.
  • Aids in refining cardiovascular risk stratification for better patient management.