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 Function Tests01:25

Pulmonary Function Tests

651
Pulmonary Function Tests (PFTs)
Pulmonary Function Tests are crucial diagnostic tools for assessing respiratory function, particularly in patients with chronic respiratory disorders. They comprehensively evaluate lung volumes, ventilatory function, breathing mechanics, diffusion, and gas exchange. These tests help diagnose pulmonary diseases and play a significant role in monitoring disease progression, evaluating disability, and assessing response to therapy.
PFTs involve using a spirometer, a...
651

You might also read

Related Articles

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

Sort by
Same author

Impact of Deep Learning-Based Denoising on Image Quality and Diagnostic Confidence in Neurovascular Ultrahigh-Resolution Photon-Counting CT Angiography.

AJNR. American journal of neuroradiology·2026
Same author

Added value of photon-counting CT for triple rule-out imaging: A propensity-matched comparison with energy-integrating CT.

Journal of cardiovascular computed tomography·2026
Same author

Field strength dependence, physiologic correlates, and prognostic significance of ventricular blood pool T2 mapping on cardiovascular magnetic resonance imaging.

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance·2026
Same author

Activity-Based Profiling of Papain-like Cysteine Proteases in Different Plant Organs During Barley Development.

Plants (Basel, Switzerland)·2026
Same author

Cardiac magnetic resonance-derived left atrioventricular coupling index predicts outcome in reduced ejection fraction.

ESC heart failure·2026
Same author

Photon-counting CT in cardiac imaging: multi-institutional guidance on technical principles, clinical evidence, and practical protocols.

European journal of radiology·2026
Same journal

The Banality of Cancer: Entropy As a Third Pillar of Lung Nodule Risk Assessment.

AJR. American journal of roentgenology·2026
Same journal

A Narrow Window for Artificial Intelligence-Generated Synthetic Temporal Bone CT From MRI.

AJR. American journal of roentgenology·2026
Same journal

From Uncertainty to Actionable Management: The Isolated Abnormal Axillary Lymph Node.

AJR. American journal of roentgenology·2026
Same journal

Beyond Detection: Translating Artificial Intelligence-Driven Opportunistic Screening Into Clinical Action.

AJR. American journal of roentgenology·2026
Same journal

Navigating PSMA PET Radiopharmaceuticals: Clinical and Operational Factors.

AJR. American journal of roentgenology·2026
Same journal

From Mesenteric Ischemia to Intestinal Stroke.

AJR. American journal of roentgenology·2026
See all related articles

Related Experiment Video

Updated: Dec 27, 2025

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

932

Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function

Andreas M Fischer1,2, Akos Varga-Szemes1, Marly van Assen1,3

  • 1Department of Radiology and Radiological Science, Division of Cardiovascular Imaging, Medical University of South Carolina, 25 Courtenay Dr MSC 226, Charleston, SC 29425.

AJR. American Journal of Roentgenology
|March 5, 2020
PubMed
Summary
This summary is machine-generated.

An AI algorithm for emphysema quantification on CT scans shows strong correlation with spirometry results. This automated tool may aid in diagnosing and assessing emphysema severity using imaging.

Keywords:
CTartificial intelligencechronic obstructive pulmonary diseaseemphysema quantificationlung function values

More Related Videos

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
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

974

Related Experiment Videos

Last Updated: Dec 27, 2025

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

932
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
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

974

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pulmonary Medicine

Background:

  • Emphysema quantification on CT scans is crucial for diagnosing and managing lung diseases.
  • Spirometry, specifically the Tiffeneau index (TI), is a standard measure of airway obstruction and emphysema severity.
  • Automated quantification methods are needed to improve efficiency and consistency in clinical practice.

Purpose of the Study:

  • To evaluate an artificial intelligence (AI)-based algorithm for automated emphysema quantification on chest CT.
  • To compare the AI algorithm's performance against spirometry-based Tiffeneau index (TI) measurements.

Main Methods:

  • A retrospective study included 141 patients who underwent both chest CT and spirometry.
  • An AI algorithm using a deep convolution image-to-image network with adversarial training was developed for lung segmentation and emphysema quantification.
  • Emphysema quantification by AI was compared with the spirometry-derived Tiffeneau index (TI) using Spearman correlation.

Main Results:

  • The AI algorithm demonstrated a very strong negative correlation with the Tiffeneau index (TI) (ρ = -0.86 and ρ = -0.85, p < 0.0001) for both CT reconstruction methods.
  • These findings indicate that the AI-based emphysema quantification meaningfully reflects clinical pulmonary physiology.
  • The mean TI was 0.57 ± 0.13, with AI-quantified emphysema percentages of 9.96% ± 11.87% and 8.04% ± 10.32%.

Conclusions:

  • AI-based, fully automated emphysema quantification on chest CT shows good correlation with spirometry.
  • This AI tool has the potential to assist in image-based diagnosis and quantification of emphysema severity.
  • Automated AI analysis offers a promising approach for objective assessment of emphysema in clinical settings.