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Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
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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-Based Carotid Plaque Ultrasound Image Detection and Classification Study.

Hongzhen Zhang1, Feng Zhao2

  • 1Precision Medicine Innovation Institute, Anhui University of Science and Technology, 232001 Huainan, Anhui, China.

Reviews in Cardiovascular Medicine
|January 1, 2025
PubMed
Summary

Deep learning models effectively detect and classify carotid plaques in ultrasound images. The Faster RCNN (ResNet 50) model shows high accuracy, aiding in stroke prevention strategies.

Keywords:
artificial intelligencecarotid plaquedeep learning techniquesischemic strokevulnerability

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Diagnostics

Background:

  • Carotid atherosclerotic plaques pose a risk for ischemic stroke.
  • Accurate detection and classification of carotid plaques are crucial for risk assessment.
  • Current screening methods can be enhanced with advanced computational tools.

Purpose of the Study:

  • To develop and evaluate deep learning models for carotid plaque detection and classification.
  • To assess the performance of different deep learning architectures on ultrasound images.
  • To enable efficient and precise ultrasound screening for carotid atherosclerotic plaques.

Main Methods:

  • A dataset of 5611 carotid ultrasound images from 3683 patients was curated.
  • Images were annotated and divided into training (3927) and test (1684) sets.
  • Four deep learning models, including YOLO V7 and Faster RCNN, were employed for detection and classification.

Main Results:

  • The Faster RCNN (ResNet 50) model achieved the best classification performance.
  • Key metrics for Faster RCNN (ResNet 50) included accuracy (0.88), sensitivity (0.94), specificity (0.71), and AUC (0.91).
  • This model significantly outperformed other evaluated deep learning approaches.

Conclusions:

  • The Faster RCNN (ResNet 50) model demonstrates high accuracy and reliability in classifying carotid plaques.
  • Its diagnostic capabilities approach those of intermediate-level physicians.
  • This technology can augment primary-level physicians' abilities and improve ischemic stroke prevention strategies.