Can an image-based electrocardiographic algorithm improve access to care
in remote settings?

Date:
March 31, 2022
Source:
Yale University
Summary:
Researchers have developed an artificial intelligence (AI)-based
model for clinical diagnosis that can use electrocardiogram (ECG)
images, regardless of format or layout, to diagnose multiple heart
rhythm and conduction disorders.



FULL STORY ========================================================================== Researchers at the Yale Cardiovascular Data Science (CarDS) Lab have
developed an artificial intelligence (AI)-based model for clinical
diagnosis that can use electrocardiogram (ECG) images, regardless of
format or layout, to diagnose multiple heart rhythm and conduction
disorders.


==========================================================================
The team led by Dr. Rohan Khera, assistant professor in cardiovascular medicine, developed a novel multilabel automated diagnosis model from
ECG images. ECG Dx (c) is the latest tool from the CarDS Lab designed to
make AI- based ECG interpretation accessible in remote settings. They
hope the new technology provides an improved method to diagnose key
cardiac disorders. The findings were published in Nature Communicationson
March 24.

The first author of the study is Veer Sangha, a computer science major at
Yale College. "Our study suggests that image and signal models performed comparably for clinical labels on multiple datasets," said Sangha. "Our approach could expand the applications of artificial intelligence to
clinical care targeting increasingly complex challenges." As mobile
technology improves, patients increasingly have access to ECG images,
which raises new questions about how to incorporate these devices in
patient care. Under Khera's mentorship, Sangha's research at the CarDS
Lab analyzes multi-modal inputs from electronic health records to design potential solutions.

The model is based on data collected from more than 2 million ECGs from
more than 1.5 million patients who received care in Brazil from 2010 to
2017. One in six patients was diagnosed with rhythm disorders. The tool
was independently validated through multiple international data sources,
with high accuracy for clinical diagnosis from ECGs.

Machine learning (ML) approaches, specifically those that use deep
learning, have transformed automated diagnostic decision-making. For
ECGs, they have led to the development of tools that allow clinicians
to find hidden or complex patterns. However, deep learning tools use signal-based models, which according to Khera have not been optimized
for remote health care settings. Image-based models may offer improvement
in the automated diagnosis from ECGs.

There are a number of clinical and technical challenges when using
AI-based applications.

"Current AI tools rely on raw electrocardiographic signals instead of
stored images, which are far more common as ECGs are often printed and
scanned as images. Also, many AI-based diagnostic tools are designed for individual clinical disorders, and therefore, may have limited utility
in a clinical setting where multiple ECG abnormalities co-occur," said
Khera. "A key advance is that the technology is designed to be smart --
it is not dependent on specific ECG layouts and can adapt to existing variations and new layouts. In that respect, it can perform like expert
human readers, identifying multiple clinical diagnoses across different
formats of printed ECGs that vary across hospitals and countries." This
study was supported by research funding from the National Heart, Lung,
and Blood Institute of the National Institutes of Health (K23HL153775).


========================================================================== Story Source: Materials provided by Yale_University. Original written
by Elisabeth Reitman.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Veer Sangha, Bobak J. Mortazavi, Adrian D. Haimovich, Anto^nio H.

Ribeiro, Cynthia A. Brandt, Daniel L. Jacoby, Wade L. Schulz,
Harlan M.

Krumholz, Antonio Luiz P. Ribeiro, Rohan Khera. Automated multilabel
diagnosis on electrocardiographic images and signals. Nature
Communications, 2022; 13 (1) DOI: 10.1038/s41467-022-29153-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220331134231.htm

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