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

Organoid-Derived Extracellular Vesicles: From Biogenesis and Cargo Mechanisms Toward Therapeutic Applications.

International journal of nanomedicine·2026
Same author

Ketone Bodies Derived From Medium-Chain Triglycerides Support Brain Metabolism and Function Under Hypoglycemia in Type 1 Diabetes Mellitus.

Diabetes·2026
Same author

Angle-sensor-assisted auto-coupling system for high-speed wide-field optical wireless communication.

Applied optics·2026
Same author

Reversible alterations of brain acetate metabolism associated with alcohol consumption.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Quantifying the Triboelectric Series of Liquid Phase Materials.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

NIR-II Fluorescent Nanoplatforms with Defect-Regulated Piezoelectricity for Dual NIR-II/MRI-Guided, Hypoxia-Resilient Piezo-Chemodynamic Therapy and cGAS-STING Activation in Orthotopic Liver Tumor.

Small (Weinheim an der Bergstrasse, Germany)·2026

Related Experiment Video

Updated: May 12, 2025

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

14.0K

nnU-Net-based high-resolution CT features quantification for interstitial lung diseases.

Qiuxi Lin1, Ziyi Zhang2, Xirui Xiong3

  • 1Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

European Radiology
|May 9, 2025
PubMed
Summary

A new AI tool, CVILDES, accurately quantifies interstitial lung disease (ILD) on CT scans, matching expert visual assessments. This computer vision system offers a reliable method for evaluating ILD progression and treatment efficacy.

Keywords:
Computed tomographyComputer assistDeep learningInterstitial lung diseases

More Related Videos

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.7K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.3K

Related Experiment Videos

Last Updated: May 12, 2025

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

14.0K
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
10:26

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

1.7K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.3K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pulmonary Medicine

Background:

  • Visual assessment of interstitial lung diseases (ILDs) on high-resolution computed tomography (HRCT) is time-consuming and suffers from poor inter-observer agreement.
  • Accurate quantification of ILD abnormalities is crucial for evaluating disease progression and therapeutic efficacy.

Purpose of the Study:

  • To develop a novel high-resolution CT (HRCT) quantification tool, CVILDES, for interstitial lung diseases (ILDs) utilizing the nnU-Net framework.
  • To validate the clinical reliability and precision of CVILDES-derived quantitative parameters against expert visual evaluation.

Main Methods:

  • A deep learning model based on nnU-Net was developed using supervised learning on HRCT scans from 83 ILD cases and 20 other diffuse lung diseases.
  • Clinical validation involved quantitative evaluation of CT parenchymal patterns in 51 interstitial pneumonia with autoimmune features (IPAF) and 14 idiopathic pulmonary fibrosis (IPF) cases using CVILDES and visual assessment.
  • Correlations between CVILDES and visual evaluations for ILD features, and between these methods and pulmonary function parameters (DLCO%, FVC%, FEV%), were analyzed.

Main Results:

  • CVILDES successfully quantified all CT data, including total ILD extent, ground-glass opacity, consolidation, reticular pattern, and honeycombing.
  • CVILDES-quantified results showed strong correlations with visual evaluation (r=0.64-0.89, p<0.0001), particularly for fibrosis extent (r=0.82, p<0.0001).
  • CVILDES quantification demonstrated comparable or superior correlation with pulmonary function parameters compared to visual evaluation.

Conclusions:

  • The nnU-Net-based CVILDES tool provides a reliable and accurate quantification of ILD abnormalities on HRCT.
  • CVILDES offers a valuable potential application for quantitative assessment of ILDs in clinical settings, addressing limitations of visual assessment.