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Related Concept Videos

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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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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Related Experiment Video

Updated: Apr 23, 2026

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
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A Fully Automated Deep Learning Model for Quantifying Coronary Plaque at Coronary CT Angiography.

Qian Chen1, Fan Zhou2, Wei Xing3

  • 1Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.

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Summary

A new deep learning model, PlaqueSegNet, accurately quantifies coronary plaque volume from CCTA scans. This automated tool shows prognostic value for predicting major adverse cardiac events.

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Area of Science:

  • Cardiovascular Imaging
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Deep learning (DL) models for coronary plaque quantification via coronary CT angiography (CCTA) are underutilized in clinical practice.
  • Developing automated DL tools is crucial for advancing cardiovascular diagnostics.

Purpose of the Study:

  • To create a fully automated DL model, PlaqueSegNet, for coronary plaque volume (PV) quantification.
  • To assess the prognostic capability of PlaqueSegNet in predicting major adverse cardiac events (MACEs).

Main Methods:

  • Developed PlaqueSegNet using a training dataset of 1409 patients undergoing CCTA.
  • Externally validated the model on four independent datasets, including paired CCTA/intravascular US (IVUS) and diverse CT scanner data.
  • Evaluated prognostic value using the Harrell C-index in three distinct patient cohorts.

Main Results:

  • PlaqueSegNet demonstrated high agreement and reproducibility (intraclass correlation coefficients >0.90) for PV quantification compared to IVUS and expert readers.
  • The model achieved C-indices ranging from 0.64 to 0.74 for predicting MACEs across different cohorts with median follow-ups of 2.3 to 5.3 years.
  • The DL model showed strong performance across various datasets, including those with different CT scanners and imaging techniques.

Conclusions:

  • PlaqueSegNet offers fully automated, accurate plaque volume measurements from CCTA.
  • The model's quantification closely aligns with expert assessments and IVUS.
  • PlaqueSegNet possesses significant prognostic value for future MACEs, supporting its potential clinical utility.