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Related Concept Videos

Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

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The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
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Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

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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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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

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A

Asifuzzaman Lasker1, Sk Md Obaidullah1, Chandan Chakraborty2

  • 1Department of Computer Science & Engineering, Aliah University, Kolkata, India.

SN Computer Science
|December 5, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence and machine learning accelerate COVID-19 diagnosis using chest X-rays and CT scans. This review summarizes AI/ML/DL applications for rapid, non-invasive disease screening.

Keywords:
COVID-19CTDeep learningMachine learningRadiological imagingX-ray

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Infectious Disease Diagnostics

Background:

  • Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) caused a global pandemic, leading to widespread respiratory illness.
  • Radiological imaging, including chest X-ray and CT, is crucial for rapid COVID-19 screening.
  • Shortages of medical experts necessitate technology-driven diagnostic solutions.

Purpose of the Study:

  • To comprehensively review the application of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in diagnosing COVID-19.
  • To summarize and compare various AI/ML/DL techniques, datasets, and outcomes for COVID-19 detection using chest X-ray and CT images.
  • To discuss the novelty, advantages, and limitations of AI-driven COVID-19 diagnostic studies.

Main Methods:

  • A systematic literature review was conducted following the PRISMA guidelines.
  • 265 relevant articles were selected from 1715 published works up to Q3 2021.
  • The review focused on studies utilizing AI/ML/DL for COVID-19 detection in radiological images.

Main Results:

  • Numerous AI/ML/DL algorithms have been developed for computer-assisted COVID-19 detection.
  • The review categorizes and compares different ML/DL techniques, datasets, and their reported performance metrics.
  • Key findings highlight the potential of AI/ML/DL in enhancing diagnostic accuracy and efficiency.

Conclusions:

  • AI/ML/DL shows significant promise for the computer-assisted diagnosis of COVID-19, particularly using chest X-ray and CT imaging.
  • The review provides a valuable overview of current research, aiding future development and clinical implementation.
  • Further research is needed to address the limitations and optimize AI models for widespread use.