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Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?

Abstract

Purpose of Review

Artificial intelligence (AI) techniques have the potential to remarkably change the practice of cardiology in order to improve and optimize outcomes in heart failure and specifically cardiomyopathies, offering us novel tools to interpret data and make clinical decisions. The aim of this review is to describe the contemporary state of AI and digital health applied to cardiomyopathies as well as to define a potential pivotal role of its application by physicians in clinical practice.

Recent Findings

Many studies have been undertaken in recent years on cardiomyopathy screening especially using AI-enhanced electrocardiography (ECG). Even with mild left ventricular (LV) dysfunction, AI-ECG screening for amyloidosis, hypertrophic cardiomyopathy, or dilated cardiomyopathy is now feasible. Introduction of AI-ECG in routine clinical care has resulted in higher detection of LV systolic dysfunction; however, clinical research on a broader scale with diverse populations is necessary and ongoing. In the area of cardiac-imaging, AI automatically assesses the thickness and characteristics of myocardium to differentiate cardiomyopathies, but research on its prognostic capability has yet to be conducted. AI is also being applied to cardiomyopathy genomics, especially to predict pathogenicity of variants and identify whether these variants are clinically actionable.

Summary

While the implementation of AI in the diagnosis and treatment of cardiomyopathies is still in its infancy, an ever-growing clinical research strategy will ascertain the clinical utility of these AI tools to help improve diagnosis of and outcomes in cardiomyopathies. We also need to standardize the tools used to monitor the performance of AI-based systems which can then be used to expedite decision-making and rectify any hidden biases. Given its potential important role in clinical practice, healthcare providers need to familiarize themselves with the promise and limitations of this technology.

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Abbreviations

AI:

Artificial intelligence

DCM:

Dilated cardiomyopathy

HCM:

Hypertrophic cardiomyopathy

RCM:

Restrictive cardiomyopathy

ARVC:

Arrhythmogenic right ventricular cardiomyopathy

CVD:

Cardiovascular disease

ML:

Machine learning

NN:

Neural network

MLP:

Multilayer perceptron

CNN:

Convolutional neural network

LSTM:

Long-short-term memory

DL:

Deep learning

ECG:

Electrocardiography

CMR:

Cardiac magnetic resonance

AUC:

Area under the curve

LVEF:

Left ventricular ejection fraction

LV:

Left ventricule

EF:

Ejection fraction

LVH:

Left ventricular hypertrophy

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Funding

Joon-myoung Kwon reports that this research was a result of a study on the “Healthcare Data Center Hospitals” Project, supported by the Ministry of Health and Welfare of South Korea and Korea Health Information Service.

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Correspondence to Naveen L. Pereira.

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Conflict of Interest

Zachi I. Attia reports Equity in ANUMANA for ECG-AI for AS, Low EF, HCM, Amyloids, and other AI-ECG algorithms. Joon-myoung Kwon is co-founder and researcher of Medical AI Inc., a medical artificial intelligence company, as well as a researcher of Body friend Co., Ltd. The other authors declare that they have no conflict of interest.

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Kim, KH., Kwon, JM., Pereira, T. et al. Artificial Intelligence Applied to Cardiomyopathies: Is It Time for Clinical Application?. Curr Cardiol Rep (2022). https://doi.org/10.1007/s11886-022-01776-4

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Keywords

  • Artificial intelligence
  • Cardiomyopathy
  • Hypertrophic cardiomyopathy
  • Dilated cardiomyopathy
  • Genetics
  • Heart failure