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Electrocardiogram

Electrocardiogram

An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...

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Detection of Hypertrophic Cardiomyopathy on Electrocardiogram Using Artificial Intelligence.

James M Hillis^1,2,3, Bernardo C Bizzo^1,4,3, Sarah F Mercaldo^1,4,3

¹Mass General Brigham AI, Boston, MA (J.M.H., B.C.B., S.F.M., A.G., A.L.M.D., M.A.H., A.S.S., E.L.I., V.T., K.J.D., B.M.S.).

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Summary

An artificial intelligence device shows promise in detecting hypertrophic cardiomyopathy (HCM) using electrocardiograms. This AI tool could aid in earlier diagnosis and improve patient outcomes for this serious heart condition.

Keywords:

artificial intelligence cardiomyopathies deep learning electrocardiography

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Area of Science:

Cardiology
Medical Artificial Intelligence
Diagnostic Tools

Background:

Hypertrophic cardiomyopathy (HCM) is a significant cause of morbidity and mortality, particularly sudden cardiac death in young individuals.
The condition is estimated to affect 1 in 500 people, with many cases remaining undiagnosed.
Improved screening methods, such as electrocardiogram (ECG) analysis, could enhance early detection and diagnosis of HCM.

Purpose of the Study:

To evaluate the accuracy of an artificial intelligence (AI) device in detecting hypertrophic cardiomyopathy (HCM) using a standard 12-lead electrocardiogram.
To assess the potential of AI in augmenting the diagnostic capabilities for HCM.

Main Methods:

A deep learning-based AI device was utilized, providing a binary output: 'HCM suspected' or 'not suspected'.
The study included a dataset of 293 HCM-positive and 2912 HCM-negative cases, identified through chart review across three hospitals.
The AI device processed 291 (99.3%) HCM-positive and 2905 (99.8%) HCM-negative cases.

Main Results:

The AI device achieved a sensitivity of 68.4% and a specificity of 99.1% for HCM detection.
The area under the curve (AUC) was 0.975, indicating strong discriminatory performance.
With an assumed prevalence of 0.2% (1 in 500), the positive predictive value was 13.7% and the negative predictive value was 99.9%.

Conclusions:

The AI device demonstrated good performance in identifying hypertrophic cardiomyopathy from 12-lead electrocardiograms.
When used in conjunction with clinical expertise, this AI tool has the potential to improve the detection and diagnosis of HCM.