New self-learning algorithm may detect blood pumping problems by reading electrocardiograms
From: The Mount Sinai Hospital / Mount Sinai School
of Medicine
October 18, 2021 -- A special artificial
intelligence (AI)-based computer algorithm created by Mount Sinai researchers
was able to learn how to identify subtle changes in electrocardiograms (also
known as ECGs or EKGs) to predict whether a patient was experiencing heart
failure.
"We showed that deep-learning
algorithms can recognize blood pumping problems on both sides of the heart from
ECG waveform data," said Benjamin S. Glicksberg, PhD, Assistant Professor
of Genetics and Genomic Sciences, a member of the Hasso Plattner Institute for
Digital Health at Mount Sinai, and a senior author of the study published in
the Journal of the American College of Cardiology: Cardiovascular
Imaging. "Ordinarily, diagnosing these type of heart conditions
requires expensive and time-consuming procedures. We hope that this algorithm
will enable quicker diagnosis of heart failure."
The study was led by Akhil Vaid, MD, a
postdoctoral scholar who works in both the Glicksberg lab and one led by Girish
N. Nadkarni, MD, MPH, CPH, Associate Professor of Medicine at the Icahn School
of Medicine at Mount Sinai, Chief of the Division of Data-Driven and Digital
Medicine (D3M), and a senior author of the study.
Affecting about 6.2 million Americans,
heart failure, or congestive heart failure, occurs when the heart pumps less
blood than the body normally needs. For years doctors have relied heavily on an
imaging technique called an echocardiogram to assess whether a patient may be
experiencing heart failure. While helpful, echocardiograms can be
labor-intensive procedures that are only offered at select hospitals.
However, recent breakthroughs in
artificial intelligence suggest that electrocardiograms -- a widely used
electrical recording device -- could be a fast and readily available
alternative in these cases. For instance, many studies have shown how a
"deep-learning" algorithm can detect weakness in the heart's left
ventricle, which pushes freshly oxygenated blood out to the rest of the body.
In this study, the researchers described the development of an algorithm that
not only assessed the strength of the left ventricle but also the right
ventricle, which takes deoxygenated blood streaming in from the body and pumps
it to the lungs.
"Although appealing, traditionally
it has been challenging for physicians to use ECGs to diagnose heart failure.
This is partly because there is no established diagnostic criteria for these
assessments and because some changes in ECG readouts are simply too subtle for
the human eye to detect," said Dr. Nadkarni. "This study represents
an exciting step forward in finding information hidden within the ECG data
which can lead to better screening and treatment paradigms using a relatively
simple and widely available test."
Typically, an electrocardiogram involves
a two-step process. Wire leads are taped to different parts of a patient's
chest and within minutes a specially designed, portable machine prints out a
series of squiggly lines, or waveforms, representing the heart's electrical
activity. These machines can be found in most hospitals and ambulances
throughout the United States and require minimal training to operate.
For this study, the researchers
programmed a computer to read patient electrocardiograms along with data
extracted from written reports summarizing the results of corresponding
echocardiograms taken from the same patients. In this situation, the written
reports acted as a standard set of data for the computer to compare with the
electrocardiogram data and learn how to spot weaker hearts.
Natural language processing programs
helped the computer extract data from the written reports. Meanwhile, special
neural networks capable of discovering patterns in images were incorporated to
help the algorithm learn to recognize pumping strengths.
"We wanted to push the state of the
art by developing AI capable of understanding the entire heart easily and
inexpensively," said Dr. Vaid.
The computer then read more than 700,000
electrocardiograms and echocardiogram reports obtained from 150,000 Mount Sinai
Health System patients from 2003 to 2020. Data from four hospitals was used to
train the computer, whereas data from a fifth one was used to test how the
algorithm would perform in a different experimental setting.
"A potential advantage of this
study is that it involved one of the largest collections of ECGs from one of
the most diverse patient populations in the world," said Dr. Nadkarni.
Initial results suggested that the
algorithm was effective at predicting which patients would have either healthy
or very weak left ventricles. Here strength was defined by left ventricle
ejection fraction, an estimate of how much fluid the ventricle pumps out with
each beat as observed on echocardiograms. Healthy hearts have an ejection
fraction of 50 percent or greater while weak hearts have ones that are equal to
or below 40 percent.
The algorithm was 94 percent accurate at
predicting which patients had a healthy ejection fraction and 87 percent
accurate at predicting those who had an ejection fraction that was below 40
percent.
However the algorithm was not as
effective at predicting which patients would have slightly weakened hearts. In
this case, the program was 73 percent accurate at predicting the patients who
had an ejection fraction that was between 40 and 50 percent.
Further results suggested that the
algorithm also learned to detect right valve weaknesses from the
electrocardiograms. In this case, weakness was defined by more descriptive
terms extracted from the echocardiogram reports. Here the algorithm was 84
percent accurate at predicting which patients had weak right valves.
"Our results suggested that this
algorithm may eventually help doctors correctly diagnose failure on either side
of the heart," Dr. Vaid said.
Finally, additional analysis suggested
that the algorithm may be effective at detecting heart weakness in all
patients, regardless of race and gender.
"Our results suggest that this
algorithm could be a useful tool for helping clinical practitioners combat
heart failure suffered by a variety of patients," added Dr. Glicksberg.
"We are in the process of carefully designing prospective trials to test
out its effectiveness in a more real-world setting."
This study was supported by the National
Institutes of Health (TR001433).
https://www.sciencedaily.com/releases/2021/10/211018172246.htm#
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