AI predicts if -- and when -- someone will have cardiac arrest
First-of-its-kind survival predictor detects patterns in heart MRIs
invisible to the naked eye

Date:
April 7, 2022
Source:
Johns Hopkins University
Summary:
A new artificial intelligence-based approach can predict,
significantly more accurately than a doctor, if and when a patient
could die of cardiac arrest. The technology, built on raw images
of patient's diseased hearts and patient backgrounds, stands to
revolutionize clinical decision making and increase survival from
sudden and lethal cardiac arrhythmias, one of medicine's deadliest
and most puzzling conditions.



FULL STORY ==========================================================================
A new artificial intelligence-based approach can predict, significantly
more accurately than a doctor, if and when a patient could die of
cardiac arrest.

The technology, built on raw images of patient's diseased hearts and
patient backgrounds, stands to revolutionize clinical decision making
and increase survival from sudden and lethal cardiac arrhythmias, one
of medicine's deadliest and most puzzling conditions.


==========================================================================
The work, led by Johns Hopkins University researchers, is detailed today
in Nature Cardiovascular Research.

"Sudden cardiac death caused by arrhythmia accounts for as many as
20 percent of all deaths worldwide and we know little about why it's
happening or how to tell who's at risk," said senior author Natalia
Trayanova, the Murray B. Sachs professor of Biomedical Engineering and Medicine. "There are patients who may be at low risk of sudden cardiac
death getting defibrillators that they might not need and then there
are high-risk patients that aren't getting the treatment they need
and could die in the prime of their life. What our algorithm can do
is determine who is at risk for cardiac death and when it will occur,
allowing doctors to decide exactly what needs to be done." The team
is the first to use neural networks to build a personalized survival
assessment for each patient with heart disease. These risk measures
provide with high accuracy the chance for a sudden cardiac death over
10 years, and when it's most likely to happen.

The deep learning technology is called Survival Study of Cardiac
Arrhythmia Risk (SSCAR). The name alludes to cardiac scarring caused by
heart disease that often results in lethal arrhythmias, and the key to
the algorithm's predictions.

The team used contrast-enhanced cardiac imagesthat visualize scar
distribution from hundreds of real patients at Johns Hopkins Hospital
with cardiac scarring to train an algorithm to detect patterns and relationships not visible to the naked eye. Current clinical cardiac image analysis extracts only simple scar features like volume and mass, severely underutilizing what's demonstrated in this work to be critical data.



==========================================================================
"The images carry critical information that doctors haven't been able to access," said first author Dan Popescu, a former Johns Hopkins doctoral student. "This scarring can be distributed in different ways and it says something about a patient's chance for survival. There is information
hidden in it." The team trained a second neural network to learn from
10 years of standard clinical patient data, 22 factors such as patients'
age, weight, race and prescription drug use.

The algorithms' predictions were not only significantly more accurate
on every measure than doctors, they were validated in tests with an
independent patient cohort from 60 health centers across the United
States, with different cardiac histories and different imaging data,
suggesting the platform could be adopted anywhere.

"This has the potential to significantly shape clinical decision-making regarding arrhythmia risk and represents an essential step towards
bringing patient trajectory prognostication into the age of artificial intelligence," said Trayanova, co-director of the Alliance for
Cardiovascular Diagnostic and Treatment Innovation. "It epitomizes the
trend of merging artificial intelligence, engineering, and medicine as the future of healthcare." The team is now working to build algorithms now to detect other cardiac diseases. According to Trayanova, the deep-learning concept could be developed for other fields of medicine that rely on
visual diagnosis.

The team from Johns Hopkins also included: Bloomberg Distinguished
Professor of Data-Intensive Computation Mauro Maggioni; Julie Shade;
Changxin Lai; Konstantino Aronis; and Katherine Wu. Other authors include:
M. Vinayaga Moorthy and Nancy Cook of Brigham and Women's Hospital; Daniel
Lee of Northwester University; Alan Kadish of Touro College and University System; David Oyyang and Christine Albert of Cedar-Sinai Medical Center.

The work was supported by National Institutes of Health grants R01HL142496
, R01HL126802, R01HL103812; Lowenstein Foundation, National Science
Foundation Graduate Research Fellowship DGE-1746891, Simons Fellowship
for 2020-2021, National Science Foundation grant IIS-1837991, Abbott Laboratories research grant. The PRE-DETERMINE study and the DETERMINE
Registry were supported by National Heart, Lung, and Blood Institute
research grant R01HL091069, St Jude Medical Inc, and St. Jude Medical Foundation.


========================================================================== Story Source: Materials provided by Johns_Hopkins_University. Original
written by Jill Rosen.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Dan M. Popescu, Julie K. Shade, Changxin Lai, Konstantinos
N. Aronis,
David Ouyang, M. Vinayaga Moorthy, Nancy R. Cook, Daniel C. Lee,
Alan Kadish, Christine M. Albert, Katherine C. Wu, Mauro Maggioni,
Natalia A.

Trayanova. Arrhythmic sudden death survival prediction using deep
learning analysis of scarring in the heart. Nature Cardiovascular
Research, 2022; DOI: 10.1038/s44161-022-00041-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220407141905.htm

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