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Related Concept Videos

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

Imaging Studies for Cardiovascular System III: X-Ray

414
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...
414

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Related Experiment Video

Updated: Dec 21, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

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Published on: December 19, 2020

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Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets.

Yujin Oh, Sangjoon Park, Jong Chul Ye

    IEEE Transactions on Medical Imaging
    |May 13, 2020
    PubMed
    Summary

    Artificial intelligence (AI) aids COVID-19 diagnosis using chest X-rays (CXRs). A novel patch-based AI model effectively diagnoses COVID-19 from CXR images, even with limited data.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Radiology

    Background:

    • The COVID-19 pandemic highlighted the need for rapid diagnostic tools.
    • Chest X-ray (CXR) analysis is crucial for COVID-19 diagnosis and patient triage.
    • Limited availability of systematic CXR datasets hinders deep neural network training for COVID-19 detection.

    Purpose of the Study:

    • To develop an effective artificial intelligence (AI) model for COVID-19 diagnosis using chest X-rays (CXRs).
    • To address the challenge of limited data for training deep neural networks during the pandemic.
    • To create a model that provides clinically interpretable results for diagnosis and triage.

    Main Methods:

    • A patch-based convolutional neural network (CNN) approach was proposed.

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  • The model was designed with a relatively small number of trainable parameters.
  • Statistical analysis of potential imaging biomarkers in CXR radiographs informed the model's design.

    • Main Results:

    • The proposed AI method achieved state-of-the-art performance in COVID-19 diagnosis from CXRs.
    • The model generated clinically interpretable saliency maps.
    • These saliency maps assist in COVID-19 diagnosis and patient triage.

    Conclusions:

    • The patch-based CNN approach is effective for COVID-19 diagnosis using CXR images.
    • The method overcomes data limitations inherent in emergent pandemics.
    • Interpretable AI models can significantly aid clinical decision-making in infectious disease outbreaks.