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Related Concept Videos

Pneumothorax-II01:27

Pneumothorax-II

104
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:
104
Pneumothorax-I01:26

Pneumothorax-I

154
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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Pleura of the Lungs01:13

Pleura of the Lungs

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The lungs are nestled in a cavity, shielded by the pleura. The pleura, a form of serous membrane, wraps around each lung. This membrane arrangement consists of two layers: the visceral and parietal pleurae. The visceral pleura lines the surface of the lungIn contrast, the parietal pleura is the outer layer and contacts to the thoracic wall, the mediastinum, and the diaphragm. The hilum is the point of connection between the visceral and parietal layers. The space between the parietal and...
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Related Experiment Video

Updated: May 8, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Pneumothorax detection and segmentation from chest X-ray radiographs using a patch-based fully convolutional

Jakov Ivan S Dumbrique1,2, Reynan B Hernandez3,4, Juan Miguel L Cruz4

  • 1Computer Vision and Machine Intelligence Group, Department of Computer Science, University of the Philippines-Diliman, Quezon City, Philippines.

Frontiers in Radiology
|December 26, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning model for accurate pneumothorax detection and segmentation in chest X-rays. The AI-powered approach enhances diagnostic efficiency, potentially improving patient outcomes in critical care settings.

Keywords:
Vision Transformerautomatic image segmentationchest X-raysconvolutional neural networkdeep learningdiagnostic radiologylung pathology detectionpneumothorax

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computer Vision

Background:

  • Pneumothorax, or air in the pleural cavity, is a critical condition requiring prompt diagnosis.
  • Chest X-rays are standard but subtle signs of pneumothorax pose diagnostic challenges.
  • Automated detection systems can aid radiologists in identifying this life-threatening condition.

Purpose of the Study:

  • To develop and evaluate a deep learning model for automated pneumothorax detection and segmentation in chest X-ray radiographs.
  • To improve the accuracy and efficiency of pneumothorax diagnosis using advanced AI techniques.
  • To address the limitations of manual interpretation of subtle pleural line displacement.

Main Methods:

  • A novel deep learning architecture combining fully convolutional neural networks (FCNNs) and Vision Transformers (ViTs) using only convolutional modules was proposed.
  • The architecture employed a patch-based encoder-decoder structure with skip connections for effective feature integration.
  • The model was trained and validated on the SIIM-ACR Pneumothorax Segmentation dataset and a newly curated dataset from The Medical City.
  • A mixed Tversky and Focal loss function was utilized to optimize model performance.

Main Results:

  • The proposed model demonstrated significantly higher accuracy in both pneumothorax detection and segmentation compared to baseline FCNNs and prior research.
  • The architecture achieved high performance while maintaining computational efficiency.
  • Ablation studies confirmed that the mixed Tversky and Focal loss function enhanced model performance over Tversky loss alone.

Conclusions:

  • The developed deep learning model shows significant potential for improving diagnostic accuracy and efficiency in pneumothorax detection.
  • The AI-driven approach can serve as a valuable tool to assist radiologists in clinical settings.
  • This research contributes to advancing automated medical image analysis for critical conditions like pneumothorax.