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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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Deep Learning for Automatic Calcium Scoring in CT: Validation Using Multiple Cardiac CT and Chest CT Protocols.

Sanne G M van Velzen1, Nikolas Lessmann1, Birgitta K Velthuis1

  • 1From the Image Sciences Institute (S.G.M.v.V., N.L., M.A.V., I.I.), Departments of Radiology (B.K.V., T.L., P.A.d.J., W.B.V.), Experimental Cardiology (I.E.M.B.), and Radiotherapy (D.H.J.G.v.d.B.), and Imaging Division (H.M.V.), University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (N.L.); Departments of Biomedical Engineering and Physics (S.G.M.v.V., I.I.) and Radiology and Nuclear Medicine (I.I.), and Amsterdam Cardiovascular Sciences (I.I.), Amsterdam University Medical Center, University of Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (N.L.); Department of Cardiology, Meander Medical Center, Amersfoort, the Netherlands (I.E.M.B.); Department of Medicine, University of Mississippi Medical Center, Jackson, Miss (A.C.); and Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tenn (J.G.T., J.J.C.).

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Summary
This summary is machine-generated.

This study shows a deep learning (DL) calcium scoring method is robust across various CT scan types for coronary artery calcium (CAC) and thoracic aorta calcification (TAC). Adding more data improved its accuracy for cardiovascular risk assessment.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Deep learning (DL) calcium scoring methods show promise but their performance across diverse CT protocols is not well-established.
  • Evaluating DL adaptability to different CT examination types is crucial for widespread clinical adoption.

Purpose of the Study:

  • To assess the performance of an automated DL calcium scoring algorithm across a broad spectrum of nonenhanced CT examinations.
  • To determine if augmenting DL training data with specific CT protocols enhances its accuracy.

Main Methods:

  • A DL algorithm was trained and tested on 7240 participants undergoing various CT scans (CAC scoring, chest CT, PET, radiation therapy planning, low-dose chest CT).
  • Coronary artery calcium (CAC) and thoracic aorta calcification (TAC) were quantified using DL and compared to manual scoring.
  • Performance was evaluated using intraclass correlation coefficients (ICCs) and κ values for cardiovascular risk stratification.

Main Results:

  • The DL algorithm demonstrated robust performance (ICCs 0.79-0.97 for CAC, 0.66-0.98 for TAC) at baseline across different CT types.
  • Performance improved with CT protocol-specific (ICCs 0.84-0.99 CAC, 0.92-0.99 TAC) and combined training data.
  • Cardiovascular risk stratification accuracy was high (κ=0.90 at baseline, increasing to 0.92 with augmented training).

Conclusions:

  • The DL calcium scoring algorithm is reliable for quantifying coronary and thoracic calcification, even with variations in CT protocols and patient populations.
  • Enhancing DL model training with protocol-specific CT images significantly improves its performance and accuracy.