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

Determining the Minimal Clinically Important Difference of the 40-Item Smell Identification Test in People With Cystic Fibrosis.

International forum of allergy & rhinology·2026
Same author

Burst Pressure and Fatigue Durability of Commercially Available Duraplasty Sealants.

International forum of allergy & rhinology·2026
Same author

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

Radiology. Cardiothoracic imaging·2026
Same author

Uncovering Cystic Fibrosis Carrier: Insights From a Heterozygous CFTR-F508del Rabbit Model.

International forum of allergy & rhinology·2026
Same author

Impact of cigarette smoke on pulmonary vein and artery volumes in those with current and former smoking status: a quantitative computed tomography analysis.

ERJ open research·2025
Same author

Reassessment of Age-Group Subanalyses in Sinus Surgery Complications in a TriNetX Database.

International forum of allergy & rhinology·2025

Related Experiment Video

Updated: Aug 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K

Clinical Validation and Extension of an Automated, Deep Learning-Based Algorithm for Quantitative Sinus CT Analysis.

C J Massey1, L Ramos1, D M Beswick2

  • 1From the Department of Otolaryngology-Head and Neck Surgery (C.J.M., L.R., V.R.R.), University of Colorado School of Medicine, Aurora, Colorado.

AJNR. American Journal of Neuroradiology
|December 20, 2022
PubMed
Summary
This summary is machine-generated.

Automated sinus CT analysis using AI offers objective quantification of chronic rhinosinusitis, correlating well with visual scoring methods for improved diagnosis and management.

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K
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

14.3K

Related Experiment Videos

Last Updated: Aug 16, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.9K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.7K
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

14.3K

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Computed tomography (CT) is vital for diagnosing chronic rhinosinusitis (CRS).
  • Objective quantification of sinus opacification via automated methods is currently lacking.
  • Convolutional neural networks (CNNs) offer potential for automated CT analysis.

Purpose of the Study:

  • To describe new measurements for automated CT analysis.
  • To clinically validate automated CT analysis in a CRS population.
  • To correlate automated CT readouts with established visual scoring systems.

Main Methods:

  • Retrospective collection of demographic and clinical data from CRS patients.
  • Automated segmentation and analysis of CT images for opacification and osteitis.
  • Correlation of automated CT metrics with Lund-Mackay, Lund-Kennedy, and Global Osteitis Scoring Scale.

Main Results:

  • The algorithm successfully segmented 100% of CT scans.
  • Strong correlation found between automated percentage opacification and Lund-Mackay score (ρ = 0.85).
  • Moderate correlations observed between automated metrics and endoscopic/osteitis scores.

Conclusions:

  • Automated sinus CT image processing provides objective measures correlating with visual scoring.
  • This technology offers a clear benefit for CRS assessment.
  • Further validation in prospective, multi-institutional settings is recommended.