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Related Concept Videos

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Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
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Chronic Obstructive Pulmonary Disease-I: Introduction01:20

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Chronic Obstructive Pulmonary Disease (COPD) is a long-lasting respiratory condition requiring continuous attention and care. It is a progressive lung disease that leads to breathing challenges due to airflow obstruction. It manifests as persistent respiratory symptoms and restricted airflow resulting from abnormalities in the airways and alveoli, usually due to long-term exposure to harmful particles or gases. COPD mainly consists of two primary conditions: emphysema and chronic bronchitis.
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Chronic bronchitis is a key phenotype of chronic obstructive pulmonary disease (COPD), characterized by airway-centered inflammation and mucus overproduction. It develops from long-term exposure to harmful particles or gases, most commonly cigarette smoke, which triggers a persistent inflammatory response.Cellular and Structural ChangesInflammation initially affects the large bronchi and later the smaller airways, with infiltration by immune cells, including neutrophils, macrophages, and...
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Chronic Obstructive Pulmonary Disease

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Chronic obstructive pulmonary disease is a common, preventable, and treatable respiratory disorder characterized by persistent symptoms and progressive airflow limitation. This limitation results from a combination of small-airway disease (obstructive bronchiolitis) and parenchymal destruction (emphysema), both driven by chronic inflammation from exposure to harmful particles or gases.The disease includes two main pathological entities: emphysema, marked by destruction of alveolar walls and...
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Related Experiment Video

Updated: Jun 13, 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

Learning COPD sensitive filters in pulmonary CT.

Lauge Sørensen1, Pechin Lo, Haseem Ashraf

  • 1Department of Computer Science, University of Copenhagen, Denmark. lauges@diku.dk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|April 30, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel texture analysis for computed tomography (CT) scans to detect chronic obstructive pulmonary disease (COPD). The new method significantly outperforms traditional approaches in identifying disease-related textural differences.

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Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models
07:56

Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models

Published on: November 11, 2020

Area of Science:

  • Radiology and Medical Imaging
  • Pulmonary Medicine
  • Computational Pathology

Background:

  • Standard emphysema analysis in CT images includes subjective visual inspection and relative area (RA) thresholding.
  • These methods have limitations: visual inspection is subjective and impractical for large datasets, while RA ignores spatial correlations in voxel intensities.

Purpose of the Study:

  • To investigate supervised learning with texture features on CT scans for discriminating chronic obstructive pulmonary disease (COPD) from healthy smokers.
  • To explore using external, non-CT based labels for training models, bypassing the need for expert-scored CT samples.

Main Methods:

  • Applied supervised learning using texture measures on random CT samples.
  • Focused on learning textural differences to distinguish COPD subjects from healthy smokers, acknowledging emphysema's likely role.
  • Used external, non-CT based measures for sample labeling.

Main Results:

  • The proposed texture-based approach achieved a 69% classification accuracy.
  • This significantly outperformed the traditional relative area (RA) method, which achieved 55% accuracy.

Conclusions:

  • Supervised learning with texture analysis on CT scans, using external labels, is a promising approach for identifying COPD.
  • This method offers a significant improvement over conventional CT-based emphysema analysis techniques.