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

Pneumothorax-II01:27

Pneumothorax-II

921
Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
Clinical Manifestations:
921
Pneumothorax-I01:26

Pneumothorax-I

1.2K
A pneumothorax is a condition where air builds up in the space between the lung and the chest wall, causing the lung to collapse. This condition arises when air enters the space between the parietal and visceral pleura, disrupting the negative pressure essential for lung inflation. This can lead to a partial or complete collapse of the lung.
Pneumothorax can be even further classified as spontaneous, traumatic, and tension pneumothorax.
1.2K

You might also read

Related Articles

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

Sort by
Same author

Computed tomography features associated with pneumothorax susceptibility in pectus excavatum: a retrospective case-control study of lobar morphometry, attenuation metrics, and machine-learning projection.

Journal of thoracic disease·2026
Same author

Near-infrared fluorescence imaging for intraoperative identification of thoracic sympathetic ganglia during VATS ganglionectomy: a randomized controlled trial.

Surgical endoscopy·2026
Same author

Thoracoscopy-Guided vs. Ultrasound-Guided Paravertebral Block in Thoracoscopic Surgery: A Non-Inferiority Randomized Trial.

Journal of clinical medicine·2025
Same author

Volume Change Measurements of the Heart and Lungs After Pectus Excavatum Repair.

Journal of clinical medicine·2025
Same author

Prediction of ipsilateral and contralateral pneumothorax using a simple chest X-ray.

Journal of thoracic disease·2025
Same author

Anatomical assessments of injectate spread stratified by the volume of the intertransverse process block at the T2 level.

Regional anesthesia and pain medicine·2024

Related Experiment Video

Updated: Jan 18, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

435

Visual and Predictive Assessment of Pneumothorax Recurrence in Adolescents Using Machine Learning on Chest CT.

Kwanyong Hyun1, Jae Jun Kim2, Kyong Shil Im3

  • 1Department of Thoracic and Cardiovascular Surgery, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea.

Journal of Clinical Medicine
|September 13, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning accurately predicts spontaneous pneumothorax recurrence in adolescents using chest CT scans. The study identified lung blebs/bullae and apical regions as key indicators for recurrence risk.

Keywords:
chest computed tomographymachine learningpredictionspontaneous pneumothorax

More Related Videos

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

2.0K

Related Experiment Videos

Last Updated: Jan 18, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

435
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

2.0K

Area of Science:

  • Pulmonary Medicine
  • Radiology
  • Artificial Intelligence in Healthcare

Background:

  • Spontaneous pneumothorax (SP) in adolescents has a high recurrence rate.
  • Surgical intervention is often used to prevent recurrence.
  • Predictive models for SP recurrence are needed, especially for conservatively managed cases.

Purpose of the Study:

  • To predict spontaneous pneumothorax recurrence in adolescents using machine learning algorithms.
  • To visualize computed tomography (CT) features associated with SP recurrence.
  • To assess the role of blebs/bullae and apical lung regions in recurrence.

Main Methods:

  • Retrospective review of 299 adolescents with conservatively managed SP.
  • Statistical analysis of clinical risk factors.
  • Application of machine learning models to chest CT images, including Gradient-weighted Class Activation Mapping (Grad-CAM) for visualization.

Main Results:

  • Recurrence occurred in 54/164 right-sided and 43/135 left-sided SP cases.
  • Presence of blebs or bullae was significantly associated with recurrence (p < 0.001).
  • Neural networks achieved high performance (AUC: 0.970 right, 0.958 left); Grad-CAM highlighted blebs/bullae and apical regions.

Conclusions:

  • Machine learning algorithms applied to chest CT accurately predict SP recurrence in adolescents.
  • Visual analysis confirms the clinical significance of blebs/bullae.
  • Apical lung regions play a crucial role in recurrence, even without visible blebs/bullae.