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Imaging Studies for Cardiovascular System V: CT01:28

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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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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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Deep Learning-based Prediction of Percutaneous Recanalization in Chronic Total Occlusion Using Coronary CT

Zhen Zhou1, Yifeng Gao1, Weiwei Zhang1

  • 1From the Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, No. 2 Anzhen Rd, Chaoyang District, Beijing 100029, China (Z.Z., Y.G., N.Z., H.W., R.W., L.X.); School of Biomedical Engineering, Sun Yat-Sen University, Guangzhou, China (W.Z., Z.G., H.Z.); Keya Medical Company, Shenzhen, China (X.H.); Department of Cardiology, Chinese PLA General Hospital, Beijing, China (S.Z.); Department of Radiology, The First Hospital of China Medical University, Shenyang, China (X.D.); Cardiovascular Research Centre, Royal Brompton Hospital, London, UK (G.Y.); National Heart and Lung Institute, Imperial College London, London, UK (G.Y.); and Department of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, Calif (K.N.).

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Summary

A deep learning model accurately predicts percutaneous coronary intervention success for chronic total occlusion lesions, improving efficiency and accuracy over manual scores. This AI tool enhances guidewire crossing predictions for better patient outcomes.

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Area of Science:

  • Cardiovascular Imaging and Intervention
  • Artificial Intelligence in Medicine
  • Interventional Cardiology

Background:

  • Coronary CT angiography (CCTA) aids in planning percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) lesions.
  • Manual prediction scores for PCI success in CTO have limitations, necessitating more efficient methods.
  • Deep learning (DL) shows promise for improving the prediction of PCI success in CTO.

Purpose of the Study:

  • To develop and evaluate a DL model for predicting guidewire crossing and PCI success in CTO lesions.
  • To compare the performance of the DL model against existing manual prediction scores using CCTA data.

Main Methods:

  • A DL model was developed using prospective CCTA data from 534 participants with CTO lesions (training set).
  • The DL model was validated on an external test set from three tertiary hospitals (186 participants).
  • Performance was assessed by comparing DL predictions for guidewire crossing (within 30 min) and PCI success against manual scores.

Main Results:

  • The DL model significantly reduced reconstruction and analysis time (85% time saving) compared to manual scores.
  • DL model demonstrated higher accuracy in predicting guidewire crossing (91.0%) and PCI success (93.7%) versus manual scores.
  • The DL model achieved high diagnostic performance with an area under the receiver operating characteristic curve of 0.96 in the external test set.

Conclusions:

  • The developed DL model accurately predicts percutaneous recanalization outcomes for CTO lesions.
  • The DL model enhances the efficiency of non-invasive grading of PCI difficulty for CTO.
  • This AI-driven approach offers a more accurate and efficient alternative to manual scoring for CTO PCI planning.