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

Anatomy of Respiratory System I: Upper Respiratory Tract01:29

Anatomy of Respiratory System I: Upper Respiratory Tract

6.2K
The upper respiratory tract plays a vital role in the respiratory system, comprising several structures that facilitate air intake and prepare air for the lungs. It also serves as the first line of defense against pathogens and particles. This tract includes the nose and nasal cavity, the oral cavity, the paranasal sinuses, and the pharynx, each with specific functions and features.
Nose and nasal cavity
The nose and nasal cavity represent the main external openings of the respiratory tract....
6.2K
Nose and Nasal Cavity01:24

Nose and Nasal Cavity

14.4K
The nose is composed of an observable exterior segment (external nose) and an internal segment within the skull known as the nasal cavity (internal nose). The external nose, visible on the face, consists of a framework of bone and hyaline cartilage enveloped in skin and muscle and lined with a mucous membrane. This structure is supported by the frontal bone, nasal bones, and maxillary bone and is supplemented by a cartilaginous framework comprising the septal nasal cartilage, lateral nasal...
14.4K

You might also read

Related Articles

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

Sort by
Same author

Cumulative adverse childhood experiences and parent-reported allergic conditions and asthma among U.S. children: A nationally representative study.

The American psychologist·2026
Same author

Risk and Protective Factors for Infection, Severe Disease, and Mortality in Epidemic Respiratory Viruses.

Allergy·2026
Same author

Chinese Position Paper on Biologic Therapy for Allergic Rhinitis.

Allergy·2026
Same author

Association Between Body Roundness Index and Hearing Loss: A Cross-Sectional Study of NHANES.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same author

COL5A1 in the tumor microenvironment predicts the prognosis of head and neck cancer.

Science progress·2025
Same author

International Evidence-Based Guidelines for Traditional Chinese Medicine Management of Allergic Rhinitis.

Allergy·2025

Related Experiment Video

Updated: Mar 16, 2026

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

3.6K

Beyond whole-image learning: anatomically partitioned deep learning models for superior sinonasal disease

Song Li1, Xiang-Hai Hu1, Song Luo1,2

  • 1Department of Otorhinolaryngology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.

European Radiology
|March 15, 2026
PubMed
Summary

Anatomically partitioned deep learning significantly improves CT diagnosis of sinonasal diseases. This approach enhances lesion characterization compared to whole-image models, showing promise for clinical use.

Keywords:
Deep learningDiagnostic systemImage segmentationNasal cavityParanasal sinus

More Related Videos

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.3K
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

255

Related Experiment Videos

Last Updated: Mar 16, 2026

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

3.6K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.3K
Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

255

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Pathology

Background:

  • Whole-image deep learning models for CT diagnosis of sinonasal diseases often underperform due to anatomical heterogeneity.
  • This limitation hinders accurate diagnosis of various pathologies within the nasal cavity and paranasal sinuses.

Purpose of the Study:

  • To investigate if anatomically partitioned deep learning enhances diagnostic accuracy for sinonasal diseases on CT scans.
  • To compare the performance of an anatomically partitioned model against a whole-image model.

Main Methods:

  • A multicenter retrospective study included 2947 CT examinations.
  • Manual segmentation of 13 anatomical regions was performed on 150 CT scans.
  • An nnU-Net v2 model automated partitioning, and disease-specific networks were trained on segmented subregions.

Main Results:

  • The anatomically partitioned model achieved an average Dice coefficient of 0.739.
  • The average AUC for the partitioned model was 0.801, significantly outperforming the whole-image model's AUC of 0.587.
  • Statistically significant AUC improvements were observed for 42 of 73 diagnostic labels, with an average absolute increase of 0.214.

Conclusions:

  • Anatomically partitioned deep learning markedly improves CT-based diagnosis of sinonasal diseases.
  • This approach provides more reliable lesion characterization than whole-image methods.
  • The findings demonstrate strong potential for routine clinical implementation of AI in sinonasal imaging.