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Related Concept Videos

Pleural Effusion I: Introduction01:25

Pleural Effusion I: Introduction

3.9K
Pleural effusion is an abnormal fluid accumulation in the pleural cavity, a narrow space between the lungs and the chest wall. It is not a disease per se but rather a symptom or indication of an underlying disease. In normal circumstances, this space contains a small amount of fluid (5 to 15 mL), a lubricant facilitating the non-frictional movement of the pleural surfaces.
There are two main types of pleural effusion: transudative and exudative. They are differentiated using Light's...
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Pleural Effusion II: Symptoms and Management01:28

Pleural Effusion II: Symptoms and Management

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Pleural Effusion Overview
A pleural effusion is the abnormal collection of fluid between the parietal and visceral pleura layers of tissue that form the lining of the lungs and chest cavity. It can occur independently or due to surrounding parenchymal diseases, such as infection, malignancy, or inflammatory conditions.
Clinical Manifestations:
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Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

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In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
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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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Respiratory System Abnormal Finding I: Inspection and Percussion01:30

Respiratory System Abnormal Finding I: Inspection and Percussion

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Respiratory system abnormalities are a significant concern in healthcare due to their potential to indicate underlying severe conditions like Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia. These abnormalities can often be detected through physical examination methods like inspection and percussion.
Inspection Findings
During an inspection, several findings may suggest the presence of respiratory distress or disease. Pursed-lip breathing, where exhalation is slowed by...
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Ultrasonography01:17

Ultrasonography

7.3K
Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
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Related Experiment Video

Updated: Jan 13, 2026

The Application of Point-of-Care Ultrasonography (POCUS) in the Management of Acute Respiratory Distress Syndrome (ARDS) in the Intensive Care Unit
08:22

The Application of Point-of-Care Ultrasonography (POCUS) in the Management of Acute Respiratory Distress Syndrome (ARDS) in the Intensive Care Unit

Published on: December 12, 2025

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Artificial Intelligence-Based Automated Analysis for Pleural Effusion Detection on Thoracic Ultrasound: A Systematic

Guido Marchi1, Luciano Gabbrielli1, Marco Gherardi1

  • 1Pulmonology Unit, Cardiothoracic and Vascular Department, University Hospital of Pisa, Via Paradisa 2, 56124 Pisa, Italy.

Diagnostics (Basel, Switzerland)
|January 10, 2026
PubMed
Summary

Artificial intelligence (AI)-assisted thoracic ultrasound (TUS) shows promise for detecting pleural effusion (PE). However, current evidence is limited by methodological weaknesses, making it insufficient for routine clinical use.

Keywords:
artificial intelligence (AI)deep learning (DL)imagingmachine learning (ML)pleural effusion (PE)thoracic ultrasound (TUS)

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Local Anesthetic Thoracoscopy for Undiagnosed Pleural Effusion
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Local Anesthetic Thoracoscopy for Undiagnosed Pleural Effusion

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Point-of-Care Lung Ultrasound in Adults: Image Acquisition
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Related Experiment Videos

Last Updated: Jan 13, 2026

The Application of Point-of-Care Ultrasonography (POCUS) in the Management of Acute Respiratory Distress Syndrome (ARDS) in the Intensive Care Unit
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The Application of Point-of-Care Ultrasonography (POCUS) in the Management of Acute Respiratory Distress Syndrome (ARDS) in the Intensive Care Unit

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Local Anesthetic Thoracoscopy for Undiagnosed Pleural Effusion
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Point-of-Care Lung Ultrasound in Adults: Image Acquisition
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Point-of-Care Lung Ultrasound in Adults: Image Acquisition

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Pulmonology

Background:

  • Pleural effusion (PE) is a common condition requiring accurate detection for effective management.
  • Thoracic ultrasound (TUS) is a primary diagnostic tool for PE, valued for its safety and portability.
  • AI-based automated analysis of TUS is being explored to enhance diagnostic accuracy and reduce interpretation variability.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of AI-assisted TUS for detecting pleural effusion.
  • To synthesize current evidence on the performance of AI algorithms in TUS for PE diagnosis.

Main Methods:

  • A systematic review adhering to PRISMA guidelines, registered with PROSPERO.
  • Searched multiple databases (MEDLINE, Scopus, Google Scholar, etc.) up to August 2025.
  • Included studies with recognized reference standards; assessed risk of bias (QUADAS-2) and certainty (GRADE).

Main Results:

  • Five studies (7565 patients) published 2021-2025 were included, utilizing various convolutional neural networks.
  • Reported sensitivity (70.6-100%), specificity (67-100%), and AUC (0.77-0.99), with performance variations for small/complex effusions and critically ill patients.
  • Studies exhibited high risk of bias due to retrospective designs and inadequate dataset separation; external validation showed performance attenuation.

Conclusions:

  • AI-assisted TUS demonstrates potential for PE detection in controlled settings but exhibits inconsistent evidence.
  • Methodological limitations, including retrospective designs and limited validation, result in low-to-moderate certainty.
  • Further robust, prospective, multicenter studies with independent validation are necessary before clinical adoption.