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Pneumothorax-II

Pneumothorax-II

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:

Pneumothorax-I

Pneumothorax-I

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.

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Related Experiment Video

Updated: Oct 12, 2025

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

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Published on: April 12, 2024

Deep multi-instance transfer learning for pneumothorax classification in chest X-ray images.

Yuchi Tian¹, Jiawei Wang², Wenjie Yang³

¹Academy of Engineering and Technology, Fudan University, Shanghai, China.

Medical Physics

|November 21, 2021

Summary

This summary is machine-generated.

This study introduces a deep learning model for detecting pneumothorax on chest X-rays, improving diagnostic accuracy and speed. The developed computer-aided diagnosis (CAD) system shows high performance, assisting radiologists in identifying this critical condition.

Keywords:

X-ray images computer-aided diagnosis deep learning pneumothorax transfer learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Radiology

Background:

Pneumothorax is a critical condition requiring prompt diagnosis.
Current manual review of chest X-rays is time-consuming and prone to errors.
Automated detection methods are needed to improve diagnostic efficiency and accuracy.

Purpose of the Study:

To develop a reliable automatic classification method for pneumothorax detection.
To assist radiologists in rapid and accurate diagnosis of pneumothorax.
To enhance the clinical workflow for pneumothorax identification.

Main Methods:

A novel residual neural network (ResNet)-based two-stage deep-learning strategy.
Local feature learning (LFL) followed by global multi-instance learning (GMIL).
Validation on private (27,955 images) and public (NIH ChestX-ray14, 112,120 images) datasets using fivefold cross-validation.

Main Results:

Achieved state-of-the-art performance on the NIH dataset.
Demonstrated high accuracy (94.4% ± 0.7%) and AUC (97.3% ± 0.5%).
Reported precision (94.2% ± 0.3%), recall (94.6% ± 1.5%), specificity (94.2% ± 0.4%), and F1-score (94.4% ± 0.7%).

Conclusions:

The proposed deep learning strategy is an effective tool for pneumothorax identification.
The computer-aided diagnosis (CAD) system assists radiologists in diagnosing pneumothorax.
The model offers a reliable and efficient solution for clinical application.