Jove
Visualize
Contact Us

Related Concept Videos

Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

198
Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and...
198
X-ray Imaging01:24

X-ray Imaging

5.3K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
5.3K
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

119
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...
119

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A new method for augmenting short time series, with application to pain events in sickle cell disease.

PLoS computational biology·2026
Same author

Research Progress on Antivirulence Agents Targeting the Accessory Gene Regulator (Agr) System of <i>Staphylococcus Aureus</i>.

Infection and drug resistance·2026
Same author

Development and evaluation of a 15-item health literacy classification model using integrated psychometric approaches in China.

BMC public health·2026
Same author

Establishment of a Padlock Probe-Based Fluorescence Quantitative PCR Method for the Detection of the SARS-CoV-2 Omicron S371L Mutation.

Journal of fluorescence·2025
Same author

Nicotine dependence, motivations, and intention to quit smoking among smoking cessation outpatients: A cross-sectional study.

Tobacco induced diseases·2025
Same author

Vaccination coverage, willingness and determinants of herpes zoster vaccine among individuals aged 50 and above in Ningbo, China: A population-based cross-sectional study.

Human vaccines & immunotherapeutics·2025
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 24, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.3K

Advancing Chest X-ray Diagnostics via Multi-Modal Neural Networks with Attention.

Douglas Townsell, Tanvi Banerjee, Lingwei Chen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    Summary

    This study introduces an AI model that improves chest X-ray diagnostics by integrating patient data with deep learning, enhancing accuracy for respiratory diseases.

    More Related Videos

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348
    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
    02:09

    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

    Published on: April 12, 2024

    519

    Related Experiment Videos

    Last Updated: May 24, 2025

    Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
    07:53

    Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

    Published on: October 13, 2023

    1.3K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    348
    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
    02:09

    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

    Published on: April 12, 2024

    519

    Area of Science:

    • Artificial Intelligence in Medicine
    • Medical Imaging Analysis
    • Respiratory Disease Diagnostics

    Background:

    • Deep learning in AI is transforming healthcare, particularly in complex medical image analysis.
    • Chest X-ray diagnostics present challenges due to diverse labels and imbalanced data for respiratory diseases.

    Purpose of the Study:

    • To develop an AI model for enhanced chest X-ray image diagnostics.
    • To improve diagnostic precision in respiratory disease cases, addressing class imbalances and complex labels.

    Main Methods:

    • Synergizing a pre-trained image classification neural network with patient and image metadata.
    • Implementing an effective decision boundary to increase accuracy and minimize false positives.

    Main Results:

    • Achieved an average Area Under the Curve (AUC) score of 0.84.
    • Demonstrated superior performance compared to existing AI models in chest X-ray diagnostics.

    Conclusions:

    • The developed AI tool significantly boosts diagnostic precision for respiratory conditions.
    • This AI model aids clinical decision-making, especially for patients with respiratory comorbidities.