Jove
Visualize
Contact Us
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 Concept Videos

Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

914
Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:
914
Classification of Illness01:17

Classification of Illness

8.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.5K
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

379
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...
379
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

FetalCLIP: a visual-language foundation model for fetal ultrasound image analysis.

NPJ digital medicine·2026
Same author

Beyond benchmarks of IUGC: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos.

Medical image analysis·2026
Same author

FUGC: Benchmarking Semi-Supervised Learning Methods for Cervical Segmentation.

IEEE transactions on medical imaging·2026
Same author

IUGC: A benchmark of landmark detection in end-to-end intrapartum ultrasound biometry.

Medical image analysis·2026
Same author

MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study.

Scientific reports·2025
Same author

SimLVSeg: Simplifying Left Ventricular Segmentation in 2-D+Time Echocardiograms With Self- and Weakly Supervised Learning.

Ultrasound in medicine & biology·2024
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
See all related articles

Related Experiment Video

Updated: Jan 12, 2026

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

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

Published on: December 19, 2020

14.7K

Weakly-supervised explainable infection severity classification from chest CT scans.

Ibrahim Almakky1, Mohammad Yaqub1

  • 1Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates.

Plos One
|October 30, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a weakly-supervised method for classifying lung infection severity from CT scans, improving diagnosis and treatment planning for respiratory diseases like COVID-19.

More Related Videos

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

420
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

471

Related Experiment Videos

Last Updated: Jan 12, 2026

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

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

Published on: December 19, 2020

14.7K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

420
A Data-Driven Approach to Quantifying Immune States in Sepsis
07:42

A Data-Driven Approach to Quantifying Immune States in Sepsis

Published on: February 7, 2025

471

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Novel respiratory diseases pose significant healthcare challenges, necessitating improved diagnostic and prognostic tools.
  • Automated lung infection severity classification from CT scans is vital for treatment decisions and infection control.
  • Current automated methods struggle with performance, generalizability, and explainability due to 3D data complexity and reliance on extensive annotations.

Purpose of the Study:

  • To develop a weakly-supervised classification approach for automated lung infection severity assessment from CT scans.
  • To provide clinicians with explainable results for better decision-making in managing respiratory infections.
  • To overcome the limitations of segmentation-based methods requiring extensive data annotation and clinical expertise.

Main Methods:

  • A weakly-supervised classification framework was developed, focusing on both low-level infection patterns and high-level infection coverage.
  • High-level features were fused with positionally encoded low-level features for volume-level infection classification.
  • The approach was tested on multi-center, multi-region SARS-CoV-2 (COVID-19) datasets.

Main Results:

  • The proposed approach achieved state-of-the-art severity classification performance on COVID-19 datasets.
  • Significant performance gains were observed on cross-site training and testing splits, indicating improved generalizability.
  • The method demonstrated quantitative and qualitative explainability, highlighting substantial infection coverage.

Conclusions:

  • Weakly-supervised learning offers a promising avenue for accurate and explainable lung infection severity classification from CT scans.
  • This approach can aid healthcare professionals in treatment strategy development and infection prevention.
  • The method shows potential for improving the management of novel respiratory diseases by enhancing diagnostic capabilities.