Lung ultrasound education: simulation and hands-on

Area of Science:

• Pulmonology
• Medical Imaging
• Ultrasound Technology

Background:

• COVID-19 (coronavirus disease 2019) poses significant risks of lung damage and respiratory failure.
• Conventional imaging like chest radiography and CT scans are standard for COVID-19 diagnosis and monitoring.
• Lung ultrasound (LUS) is emerging as a complementary tool for patient management during the pandemic.

Purpose of the Study:

• To discuss the current and future role of Lung Ultrasound (LUS) in managing patients with COVID-19 and other lung diseases.
• To explore strategies for enhancing LUS education and training amidst infectious disease protocols.
• To examine how technological advancements can improve LUS accuracy and clinical integration.

Main Methods:

• This commentary reviews the application and potential of LUS in the context of COVID-19.
• It discusses the challenges in LUS training and competence assessment for infectious patients.
• It explores the use of simulation, numerical methods, and machine learning to enhance LUS practice.

Main Results:

• LUS can aid decision-making and improve patient care for suspected COVID-19 and other lung conditions.
• A significant limitation is the need for skilled practitioners for accurate LUS assessments.
• Simulation and machine learning show promise in improving LUS accuracy and reducing the learning curve.

Conclusions:

• Increased adoption of LUS can enhance clinical practice for lung disease diagnosis and management.
• Robust education, training, and technological integration are crucial for maximizing LUS utility.
• Future directions include leveraging simulation and AI to standardize and improve LUS proficiency.