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Pneumothorax-II01:27

Pneumothorax-II

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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

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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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X-ray Imaging01:24

X-ray Imaging

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Assessment of Respiration01:23

Assessment of Respiration

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The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

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Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
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Updated: Nov 6, 2025

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
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Searching for pneumothorax in x-ray images using autoencoded deep features.

Antonio Sze-To1, Abtin Riasatian1, H R Tizhoosh2,3

  • 1Kimia Lab, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.

Scientific Reports
|May 11, 2021
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This study introduces AutoThorax-Net, an AI system for searching chest X-ray archives to aid in pneumothorax diagnosis. Image search using AutoThorax-Net features shows high performance, assisting radiologists in detecting collapsed lungs.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computer-Aided Diagnosis

Background:

  • Pneumothorax (collapsed lung) diagnosis from chest X-rays is critical but challenging for radiologists, especially for subtle cases.
  • Current deep learning methods struggle with clinical utility due to limitations in high-quality labeled datasets.
  • Automated systems are needed to assist radiologists and improve diagnostic accuracy and speed.

Purpose of the Study:

  • To develop and evaluate an AI-powered image search system for assisting in the detection of pneumothorax on chest X-rays.
  • To leverage a large repository of chest X-ray images for training and validation.
  • To provide an explainable diagnostic assistant through image retrieval and majority voting.

Main Methods:

  • Developed the Autoencoding Thorax Net (AutoThorax-Net) for extracting deep features from chest radiographs.
  • Implemented an image search system utilizing these deep features to query a large archive of chest X-ray images.
  • Evaluated performance using Area Under the Curve (AUC) accuracy for both semi-automated and fully automated search scenarios.

Main Results:

  • Achieved 92% AUC accuracy for semi-automated search in a dataset of 194,608 images (pneumothorax and normal).
  • Attained 82% AUC accuracy for fully automated search across 551,383 images, including normal, pneumothorax, and other chest diseases.
  • Demonstrated high identification performance of image search based on AutoThorax-Net features.

Conclusions:

  • AutoThorax-Net facilitates effective image search in large chest X-ray repositories, offering a promising approach for clinical assistance.
  • The developed system shows potential for real-world deployment as a diagnostic aid for pneumothorax detection.
  • Image search with deep features provides a viable alternative to traditional deep learning classifiers, especially when labeled data is scarce.