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Related Concept Videos

Pneumothorax-II01:27

Pneumothorax-II

788
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.
1.1K

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Related Experiment Video

Updated: Jan 3, 2026

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Crowdsourcing pneumothorax annotations using machine learning annotations on the NIH chest X-ray dataset.

Ross W Filice1, Anouk Stein2, Carol C Wu3

  • 1Department of Radiology, MedStar Georgetown University Hospital, 3800 Reservoir Road, NW CG201, Washington, DC, 20007, USA. ross.w.filice@gunet.georgetown.edu.

Journal of Digital Imaging
|November 27, 2019
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can aid in detecting pneumothorax on chest X-rays, potentially speeding up diagnosis in ICUs. A new, publicly available dataset and machine learning annotation methods aim to advance AI for this critical condition.

Keywords:
Artificial intelligenceChallengeChest radiographMachine learning annotationsPneumothoraxPublic datasets

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Thoracic radiology

Background:

  • Pneumothorax is a critical condition requiring rapid diagnosis and intervention.
  • Interpreting chest radiographs in the ICU is time-consuming and can delay pneumothorax detection.
  • AI holds potential for expediting, localizing, and quantifying pneumothorax on radiographs.

Purpose of the Study:

  • To create and release a large, expert-adjudicated dataset of chest radiographs for public use.
  • To investigate the utility of machine learning annotation (MLA) in expediting the dataset creation process.
  • To foster innovation in AI algorithms for pneumothorax detection.

Main Methods:

  • Annotation and adjudication of a large chest radiograph dataset by experts.
  • Exploration of machine learning annotation (MLA) techniques to assist in labeling.
  • Dataset made publicly available as a challenge to the research community.

Main Results:

  • A substantial, adjudicated dataset for pneumothorax detection is now publicly accessible.
  • Machine learning annotation (MLA) demonstrated potential to accelerate the annotation workflow.
  • MLA achieved high sensitivity but lower specificity in generating initial annotations.

Conclusions:

  • The developed dataset and challenge aim to drive advancements in AI for pneumothorax detection.
  • Machine learning annotation shows promise for improving the efficiency of medical image labeling.
  • Further research is needed to fully validate and optimize MLA techniques for clinical application.