Artificial intelligence in echocardiography: current applications and future perspectives.

Area of Science:

• Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Echocardiography faces challenges including operator dependency and inter-observer variability.
• Artificial intelligence (AI), particularly deep learning, offers potential solutions to these limitations.

Purpose of the Study:

• To explore the transformative impact of AI on echocardiography.
• To highlight AI's role in enhancing image acquisition, interpretation, and diagnostic accuracy.

Main Methods:

• Leveraging machine learning and deep learning algorithms for echocardiographic analysis.
• Developing AI-enabled systems for probe guidance and automated quantification.

Main Results:

• AI improves image quality and reduces variability in key measurements like left ventricular ejection fraction (LVEF).
• AI demonstrates comparable accuracy to expert assessment in disease classification and prediction.
• AI tools assist in detecting regional wall motion abnormalities.

Conclusions:

• AI is a valuable tool for augmenting echocardiography, not replacing human expertise.
• Addressing data bias, generalizability, and ethical concerns is crucial for clinical adoption.
• Future integration of AI with clinical data promises earlier diagnosis and improved patient management.