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Related Concept Videos

Pleural Effusion I: Introduction01:25

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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.
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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Pleural Effusion Overview
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Pleura of the Lungs01:13

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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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Pneumonia poses the potential for numerous complications that warrant consideration. These complications include the following:
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Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.

Ferhan Karataş1, Öner Dikensoy1,2

  • 1Department of Pulmonary Diseases, Koç University Hospital, İstanbul, Türkiye.

Thoracic Research and Practice
|January 15, 2026
PubMed
Summary

Artificial intelligence (AI) significantly enhances the diagnosis of pleural diseases, including pleural effusion and malignant pleural mesothelioma. AI models show high accuracy, potentially revolutionizing diagnostic management.

Keywords:
Artificial intelligencediagnostic methodsmesotheliomapleural diseasespneumothoraxtuberculosis

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Area of Science:

  • Pulmonology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Pleural diseases present diagnostic challenges and high healthcare costs.
  • Artificial intelligence (AI) offers potential for faster, more accurate diagnoses.
  • This review focuses on AI applications in diagnosing pleural effusion, malignant pleural effusion, tuberculosis pleurisy, pneumothorax, and malignant pleural mesothelioma.

Purpose of the Study:

  • To review the current status of AI technologies in diagnosing various pleural diseases.
  • To evaluate the effectiveness of AI in radiological, cytological, and integrated diagnostic approaches.
  • To identify the potential impact of AI on the future management of pleural diseases.

Main Methods:

  • Review of deep learning algorithms for radiological diagnosis of pleural effusion.
  • Analysis of AI models integrating clinical parameters (CT, PET-CT, tumor markers) for effusion classification.
  • Evaluation of computer-assisted systems in cytological diagnosis.
  • Assessment of AI models for tuberculosis pleurisy, pneumothorax, and malignant pleural mesothelioma diagnosis.

Main Results:

  • Deep learning shows high sensitivity and specificity for pleural effusion detection.
  • AI models integrating clinical data achieve high accuracy (>0.90 AUC) in distinguishing benign from malignant effusions.
  • AI demonstrates comparable performance to experts in cytological diagnosis and outperforms conventional methods for tuberculosis pleurisy.
  • FDA-approved AI models facilitate rapid pneumothorax diagnosis; AI aids in MPM prognostic evaluation and treatment monitoring.

Conclusions:

  • AI demonstrates significant potential to improve diagnostic accuracy and efficiency across a range of pleural diseases.
  • Further large-scale, multi-center studies are necessary to standardize and generalize AI models.
  • AI is poised to fundamentally transform the diagnostic management of pleural diseases.