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Related Concept Videos

Pneumothorax-II01:27

Pneumothorax-II

821
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:
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Pneumothorax-I01:26

Pneumothorax-I

1.1K
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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Updated: Jan 6, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Deep learning-enabled system for rapid pneumothorax screening on chest CT.

Xiang Li1, James H Thrall1, Subba R Digumarthy1

  • 1Massachusetts General Hospital, Department of Radiolgoy, United States.

European Journal of Radiology
|October 5, 2019
PubMed
Summary
This summary is machine-generated.

A deep learning (DL) program accurately detects pneumothorax on CT scans. This AI tool shows high sensitivity and specificity, aiding rapid diagnosis for critical patient management.

Keywords:
Chest CTDeep learningPneumothorax

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Prompt diagnosis of pneumothorax is crucial for patient management.
  • Chest CT is a primary imaging modality for diagnosing pneumothorax.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) based image classification program for pneumothorax detection on chest CT.
  • To assess the accuracy, sensitivity, and specificity of the DL program.

Main Methods:

  • An eight-layer convolutional neural network (CNN) was trained on 80 chest CT scans (50 with pneumothorax, 30 without).
  • The CNN classified 2D image patches and generated 3D heat-maps including size, location, and shape descriptors.
  • A support vector machine (SVM) was used for final classification.

Main Results:

  • The DL program achieved 100% sensitivity and 82.5% specificity in a test set of 200 chest CTs.
  • All 160 pneumothoraces in the test set were detected.
  • False positives were mainly due to emphysema or imaging artifacts.

Conclusions:

  • The DL-based program demonstrates high accuracy for automatic pneumothorax detection on chest CT.
  • Integration with high-performance computing and radiologist expertise can enable rapid pre-screening of cases.
  • This AI tool can assist in the rapid identification of pneumothorax, a critical finding.