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Related Concept Videos

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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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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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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Updated: Sep 6, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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COVID-19 severity detection using machine learning techniques from CT-images.

A L Aswathy1, Hareendran S Anand2, S S Vinod Chandra1

  • 1Department of Computer Science, University of Kerala, Trivandrum, Kerala India.

Evolutionary Intelligence
|June 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel two-step method using AI to detect COVID-19 from lung CT scans and assess disease severity. The approach accurately identifies infections and categorizes severity, aiding in prioritizing high-risk patients.

Keywords:
AlexNetComputed tomographyDenseNet-201Neural networkResNet-50Transfer learning

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Pulmonology

Background:

  • COVID-19, a global pandemic, significantly impacts lung health, causing severe respiratory distress.
  • Accurate differentiation of COVID-19 from other lung conditions and precise severity assessment are critical for patient management.
  • Current diagnostic challenges necessitate advanced tools for timely and effective intervention.

Purpose of the Study:

  • To develop and validate a two-step computational approach for detecting COVID-19 infection from lung CT images.
  • To determine the severity of COVID-19 illness using integrated image and clinical data.
  • To improve patient care by enabling focused attention on high-risk individuals based on severity classification.

Main Methods:

  • Feature extraction from lung CT images using pre-trained models: AlexNet, DenseNet-201, and ResNet-50.
  • COVID-19 detection using an Artificial Neural Network (ANN) model.
  • Severity classification (High, Moderate, Low) via Cubic Support Vector Machine (SVM) integrating image features and clinical data.

Main Results:

  • Achieved 92.0% accuracy, 96.0% sensitivity, and 91.44% F1-Score for COVID-19 detection.
  • Attained 90.0% overall accuracy for three-class COVID-19 severity detection.
  • Demonstrated the efficacy of the integrated approach in identifying infection and grading severity.

Conclusions:

  • The proposed two-step method effectively detects COVID-19 infection and classifies its severity from lung CT scans.
  • The integration of deep learning models and clinical data offers a robust solution for managing COVID-19 patients.
  • This AI-driven approach can aid clinicians in prioritizing care for patients with severe conditions.