Advanced imaging techniques and artificial intelligence in pleural diseases: a narrative review

Area of Science:

• Pulmonology
• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• Pleural diseases affect over 350,000 US patients annually, posing a significant healthcare challenge.
• Current diagnostic methods often rely on invasive procedures due to limitations in traditional imaging.
• Accurate and timely diagnosis is crucial for effective management of pleural disorders.

Purpose of the Study:

• To review the latest advancements in imaging techniques for pleural diseases.
• To explore the application of artificial intelligence (AI) in diagnosing and managing pleural conditions.
• To highlight novel approaches that improve diagnostic accuracy and reduce invasiveness.

Main Methods:

• Nonsystematic, narrative literature review.
• Focused on novel ultrasound techniques (elastography, contrast-enhanced ultrasound).
• Examined quantitative imaging and AI algorithms for pleural abnormality detection and characterization.

Main Results:

• Novel ultrasound techniques show promise in differentiating malignant from benign pleural lesions.
• Quantitative imaging aids in noninvasively distinguishing effusion types.
• AI algorithms demonstrate high performance in detection, characterization, and quantification of pleural abnormalities.

Conclusions:

• Advanced imaging and AI offer improved diagnostic accuracy and reduced need for invasive procedures in pleural diseases.
• Larger prospective studies are required for clinical validation of these techniques.
• Integration of AI with molecular data may enable personalized therapies, revolutionizing pleural disease management.