Deep Learning for Echocardiography: Introduction for Clinicians and Future Vision: State-of-the-Art Review

Area of Science:

• Cardiovascular Imaging
• Clinical Informatics
• Artificial Intelligence

Background:

• Exponential growth in data storage and computational power facilitates clinical informatics advancements.
• Cardiovascular imaging yields vast data, but interpretation requires specialized expertise.
• Echocardiographic data present classification challenges due to low signal-to-noise ratio.

Purpose of the Study:

• To review state-of-the-art deep learning (DL) techniques for cardiovascular image and video classification.
• To explore the potential of DL in automating tasks and extracting clinically useful data from echocardiograms.
• To discuss future directions for DL in echocardiographic research, including contactless examinations.

Main Methods:

• Review of current deep learning architectures and methodologies applicable to image and video classification.
• Focus on DL's capability to address the low signal-to-noise ratio inherent in echocardiographic data.
• Exploration of DL's role in automating conventional human interpretation tasks in cardiovascular imaging.

Main Results:

• Deep learning shows significant promise in overcoming classification challenges in echocardiographic data.
• DL architectures can automate complex interpretation tasks, potentially improving efficiency and accuracy.
• The application of DL extends towards novel applications like contactless echocardiographic examinations.

Conclusions:

• Deep learning offers a powerful approach to harness the wealth of cardiovascular imaging data.
• DL can catalyze the extraction of clinically relevant information, aiding clinicians and researchers.
• Future research in DL for echocardiography is crucial for advancing diagnostic capabilities and patient care.