Deep Learning Applications in 12-lead Electrocardiogram and Echocardiogram.

Abstract

Artificial intelligence (AI), empowered by advances in deep learning technology, has demonstrated its capabilities in the medical field to automate tedious tasks that are otherwise performed by humans or to detect or predict diseases with higher accuracy compared with experts. Given the ability to take complex multidimensional data as input, AI models have primarily been applied to complex medical imaging and time-series data. Another prominent strength of AI applications is its large scalability. The field of cardiovascular medicine uses various noninvasive and accessible metrics that produce a large amount of complex multidimensional data, such as electrocardiograms (ECGs) and echocardiograms. AI models can increase the utility of such modalities. Simple automation of conventional tasks using AI models provides significant opportunities for cost reduction and capacity expansion. The ability to improve disease detection or prediction at scale may provide novel opportunities for disease screening, enabling early intervention in asymptomatic patients. For example, AI-enabled pipelines can accurately identify cardiomyopathies and congenital heart diseases from a single ECG or echocardiogram recording. The detection of these diseases using the conventional approach usually requires complicated diagnostic strategies or expensive tests. Therefore, underdiagnosis is a huge problem. Using AI models to screen these diseases will provide opportunities for reducing missed cases. The utility of AI models in the medical field is not limited to the development of clinically useful models. Recent research has shown the promise of AI models in mechanism research by combining them with genetic and structural analyses. In this review, we provide an update on the current achievements of the innovative AI application for ECG and echocardiogram and provide insights into the future direction of AI in cardiovascular care and research settings.