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

Geographical, institutional and workplace variability in genetic evaluation, screening practices, multidisciplinary care, and lung transplantation management for familial interstitial lung disease.

BMC pulmonary medicine·2026
Same author

Co-designing a Palliative Care Referral Tool for Patients with Fibrosing Interstitial Lung Disease.

Annals of the American Thoracic Society·2026
Same author

Rheumatoid arthritis-associated interstitial lung disease: screening, diagnosis, and treatment-an expert group consensus statement.

The Lancet. Respiratory medicine·2026
Same author

Idiopathic Pulmonary Fibrosis update, comparing the Australasian Interstitial Lung Disease Registry to the Australian Idiopathic Pulmonary Fibrosis Registry.

Internal medicine journal·2026
Same author

Idiopathic pulmonary fibrosis risk loci in East Asian populations mirror those of European populations.

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

A statistical model for lung function trajectory and mortality in patients with fibrotic interstitial lung disease.

American journal of respiratory and critical care medicine·2026

Related Experiment Video

Updated: Apr 3, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
04:44

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

21.1K

Deep-Learning Algorithm Diagnostic Support for Usual Interstitial Pneumonia Pattern Recognition in Fibrotic

Caitlin C Fermoyle1, John A Mackintosh2, Vidya Navaratnam3,4

  • 1Faculty of Medicine and Health, University of Sydney, Sydney, Australia.

Respirology (Carlton, Vic.)
|April 2, 2026
PubMed
Summary
This summary is machine-generated.

The Systematic Objective Fibrotic Imaging Analysis Algorithm (SOFIA) improved diagnostic agreement and prognostic accuracy for usual interstitial pneumonia (UIP) classification on high-resolution computed tomography (HRCT) scans. This deep learning tool enhances consistency in assessing fibrotic interstitial lung disease (ILD).

Keywords:
deep learningdisease progressionidiopathic pulmonary fibrosisradiologyusual interstitial pneumonia

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

1.1K
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

1.4K

Related Experiment Videos

Last Updated: Apr 3, 2026

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease
04:44

Imaging Features of Systemic Sclerosis-Associated Interstitial Lung Disease

Published on: June 16, 2020

21.1K
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

1.1K
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

1.4K

Area of Science:

  • Pulmonology
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Classification of usual interstitial pneumonia (UIP) on high-resolution computed tomography (HRCT) scans is crucial for interstitial lung disease (ILD) patient management and clinical trials.
  • Current HRCT-based UIP classification by clinicians exhibits significant variability, impacting patient care and research.
  • The Systematic Objective Fibrotic Imaging Analysis Algorithm (SOFIA) is a deep learning tool designed to assist in HRCT classification according to established guidelines.

Purpose of the Study:

  • To evaluate the impact of the SOFIA deep learning algorithm on inter-observer agreement for UIP classification.
  • To assess the effect of SOFIA on the prognostic accuracy of clinicians' assessments of ILD HRCT scans.
  • To determine if SOFIA can improve diagnostic consistency in fibrotic ILD.

Main Methods:

  • 203 HRCT scans from a national fibrotic ILD registry were evaluated by 312 radiologists and pulmonologists.
  • Reviewers initially scored HRCTs for four UIP categories (definite, probable, indeterminate, alternative diagnosis).
  • SOFIA outputs were provided, allowing reviewers to re-evaluate their scores, with changes in agreement and prognostic accuracy calculated.

Main Results:

  • Inter-observer agreement for definite UIP improved from moderate (ICC=0.54) to good (ICC=0.70) post-SOFIA.
  • Agreement for probable UIP improved from fair (ICC=0.30) to moderate (ICC=0.53) after using SOFIA.
  • SOFIA integration led to improved prognostic accuracy for definite UIP, probable UIP, and indeterminate scores, significantly increasing the predictive value of probable UIP for transplant-free survival by 42%.

Conclusions:

  • The SOFIA algorithm significantly enhanced diagnostic agreement among clinicians assessing fibrotic ILD HRCT scans.
  • Providing SOFIA outputs improved the prognostic accuracy of clinicians' assessments, aiding in predicting patient survival.
  • SOFIA serves as a valuable automated assistive tool, promoting greater diagnostic consistency in the evaluation of fibrotic ILD.