Deep Learning Applications in 12-lead Electrocardiogram and Echocardiogram

Area of Science:

• Cardiovascular Medicine
• Medical Technology
• Artificial Intelligence

Background:

• Deep learning-powered AI is transforming healthcare by automating tasks and enhancing disease detection accuracy.
• Cardiovascular medicine generates vast, complex data from noninvasive tests like electrocardiograms (ECGs) and echocardiograms.
• AI's scalability and ability to process multidimensional data offer significant potential in cardiology.

Purpose of the Study:

• To review current AI applications in analyzing ECGs and echocardiograms.
• To highlight AI's role in automating cardiovascular tasks and improving disease detection.
• To explore future directions for AI in cardiovascular care and research.

Main Methods:

• Review of recent advancements in AI for medical imaging and time-series data analysis.
• Focus on AI applications in electrocardiogram (ECG) and echocardiogram interpretation.
• Integration of AI with genetic and structural analyses for mechanism research.

Main Results:

• AI models demonstrate high accuracy in detecting cardiomyopathies and congenital heart diseases from ECGs and echocardiograms.
• AI offers cost reduction and capacity expansion through automation of conventional tasks.
• AI facilitates large-scale disease screening, enabling early intervention in asymptomatic patients and reducing underdiagnosis.

Conclusions:

• AI significantly enhances the utility of ECGs and echocardiograms in cardiovascular medicine.
• AI-driven screening holds promise for early detection and intervention, addressing underdiagnosis challenges.
• AI is poised to play a crucial role in both clinical cardiovascular care and fundamental research settings.