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

You might also read

Related Articles

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

Sort by
Same author

Progressive pulmonary fibrosis: a state-of-the-art review.

The European respiratory journal·2026
Same author

Early Career Perspective: Beyond Quantification: Toward Predicting Regional Emphysema Progression at CT.

Radiology·2026
Same author

Occupational inhaled exposures and risk of interstitial lung abnormalities in individuals with potential familial susceptibility to pulmonary fibrosis.

Thorax·2026
Same author

Precise Lung Density Quantification with a Physics-based CT Harmonizer.

Radiology. Cardiothoracic imaging·2026
Same author

Rebuttal to "Bronchiolocentric interstitial pneumonia is a more accurate interstitial lung disease classification than hypersensitivity pneumonitis: pro".

American journal of respiratory and critical care medicine·2026
Same author

Bronchiolocentric interstitial pneumonia is a more accurate interstitial lung disease classification than hypersensitivity pneumonitis: con.

American journal of respiratory and critical care medicine·2026

Related Experiment Video

Updated: Jan 2, 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

14.6K

Deep Learning Enables Automatic Classification of Emphysema Pattern at CT.

Stephen M Humphries1, Aleena M Notary1, Juan Pablo Centeno1

  • 1From the Department of Radiology (S.M.H., A.M.N., J.P.C., D.A.L.), Division of Biostatistics and Bioinformatics (M.J.S.), and Department of Medicine (J.D.C.), National Jewish Health, 1400 Jackson St, Denver, CO 80206-2761; and Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Mass (E.K.S.).

Radiology
|December 4, 2019
PubMed
Summary
This summary is machine-generated.

A deep learning tool can automatically classify emphysema patterns on chest CT scans, predicting lung function impairment and mortality risk in patients with chronic obstructive pulmonary disease (COPD). This AI-driven approach offers a more accurate assessment than visual scoring.

More Related Videos

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

778
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

377

Related Experiment Videos

Last Updated: Jan 2, 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

14.6K
Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
03:38

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models

Published on: June 20, 2025

778
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

377

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pulmonary Medicine

Background:

  • Emphysema pattern on chest CT, assessed visually using the Fleischner Society system, correlates with physiological impairment and mortality.
  • Accurate classification of emphysema patterns is crucial for predicting disease progression and outcomes.

Purpose of the Study:

  • To develop and validate a deep learning (DL) algorithm for classifying emphysema patterns on chest CT.
  • To determine if DL-based emphysema classification can predict pulmonary function impairment and mortality risk.

Main Methods:

  • A DL algorithm (CNN and LSTM) was trained on CT scans from the Genetic Epidemiology of COPD (COPDGene) study to classify emphysema according to Fleischner criteria.
  • The DL algorithm's performance was compared with visual scoring and validated in an independent cohort (ECLIPSE study).
  • Cox proportional hazard models assessed the association between DL-emphysema scores and mortality.

Main Results:

  • The DL algorithm accurately classified emphysema patterns, showing strong associations with impaired pulmonary function tests, reduced 6-minute walk distance, and lower quality of life (St George's Respiratory Questionnaire).
  • DL-based classification demonstrated superior predictive performance for clinical parameters compared to visual scoring in the COPDGene cohort.
  • In both COPDGene and ECLIPSE cohorts, DL-identified emphysema was significantly associated with increased mortality risk across all grades.

Conclusions:

  • Deep learning automation of emphysema grading on chest CT provides a robust and accurate method for assessing disease severity.
  • This AI-driven approach is associated with clinical measures of pulmonary insufficiency and predicts mortality risk in COPD patients.
  • Automated emphysema pattern classification holds promise for improving patient stratification and guiding clinical management in COPD.