New artificial intelligence tool detects often overlooked heart diseases


Date:
February 23, 2022
Source:
Cedars-Sinai Medical Center
Summary:
Physician-scientists have created an artificial intelligence (AI)
tool that can effectively identify and distinguish between two
life- threatening heart conditions that are often easy to miss:
hypertrophic cardiomyopathy and cardiac amyloidosis.



FULL STORY ========================================================================== Physician-scientists in the Smidt Heart Institute at Cedars-Sinai
have created an artificial intelligence (AI) tool that can effectively
identify and distinguish between two life-threatening heart conditions
that are often easy to miss: hypertrophic cardiomyopathy and cardiac amyloidosis. The new findings were published in JAMA Cardiology.


========================================================================== "These two heart conditions are challenging for even expert cardiologists
to accurately identify, and so patients often go on for years to
decades before receiving a correct diagnosis," said David Ouyang,
MD, a cardiologist in the Smidt Heart Institute and senior author of
the study. "Our AI algorithm can pinpoint disease patterns that can't
be seen by the naked eye, and then use these patterns to predict the
right diagnosis." The two-step, novel algorithm was used on over 34,000 cardiac ultrasound videos from Cedars-Sinai and Stanford Healthcare's echocardiography laboratories. When applied to these clinical images,
the algorithm identified specific features - - related to the thickness
of heart walls and the size of heart chambers -- to efficiently flag
certain patients as suspicious for having the potentially unrecognized
cardiac diseases.

"The algorithm identified high-risk patients with more accuracy than
the well- trained eye of a clinical expert," said Ouyang. "This is
because the algorithm picks up subtle cues on ultrasound videos that distinguish between heart conditions that can often look very similar to
more benign conditions, as well as to each other, on initial review."
Without comprehensive testing, cardiologists find it challenging to
distinguish between similar appearing diseases and changes in heart
shape and size that can sometimes be thought of as a part of normal
aging. This algorithm accurately distinguishes not only abnormal from
normal, but also between which underlying potentially life-threatening
cardiac conditions may be present -- with warning signals that are now detectable well before the disease clinically progresses to the point
where it can impact health outcomes. Getting an earlier diagnosis enables patients to begin effective treatments sooner, prevent adverse clinical
events, and improve their quality of life.

Cardiac amyloidosis, often called "stiff heart syndrome," is a disorder
caused by deposits of an abnormal protein (amyloid) in the heart
tissue. As amyloid builds up, it takes the place of healthy heart muscle, making it difficult for the heart to work properly. Cardiac amyloidosis
often goes undetected because patients might not have any symptoms,
or they might experience symptoms only sporadically.



==========================================================================
The disease tends to affect older, Black men or patients with cancer or diseases that cause inflammation. Many patients belong to underserved communities, making the study results an important tool in improving
healthcare equity, Ouyang said.

Hypertrophic cardiomyopathy is a disease that causes the heart muscle to thicken and stiffen. As a result, it's less able to relax and fill with
blood, resulting in damage to heart valves, fluid buildup in the lungs,
and abnormal heart rhythms.

Although separate and distinct conditions, cardiac amyloidosis and
hypertrophic cardiomyopathy often look very similar to each other on an echocardiogram, the most commonly used cardiac imaging diagnostic.

Importantly, in the very early stages of disease, each of these cardiac conditions can also mimic the appearance of a non-diseased heart that
has progressively changed in size and shape with aging.

"One of the most important aspects of this AI technology is not only
the ability to distinguish abnormal from normal, but also to distinguish between these abnormal conditions, because the treatment and management
of each cardiac disease is very different," said Ouyang.

The hope, Ouyang said, is that this technology can be used to identify
patients from very early on in their disease course. That's because
clinicians know that earlier is always better for getting the most
benefit from therapies that are available today and that can be very
effective for preventing the worst possible outcomes, such as heart
failure, hospitalizations, and sudden death.

Researchers plan to soon launch clinical trials for patients flagged by
the AI algorithm for suspected cardiac amyloidosis. Patients enrolled
in the trial will be seen by experts in the cardiac amyloidosis program
at the Smidt Heart Institute, one of only a handful of programs on the
West Coast dedicated to the disease.

A clinical trial for patients flagged by the algorithm for suspected hypertrophic cardiomyopathy just started at Cedars-Sinai.

"The use of artificial intelligence in cardiology has evolved rapidly and dramatically in a relatively short period of time," said Susan Cheng,
MD, MPH, director of the Institute for Research on Healthy Aging in
the Department of Cardiology at the Smidt Heart Institute and co-senior
author of the study.

"These remarkable strides -- which span research and clinical care --
can make a tremendous impact in the lives of our patients.

========================================================================== Story Source: Materials provided by Cedars-Sinai_Medical_Center. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Grant Duffy, Paul P. Cheng, Neal Yuan, Bryan He, Alan C. Kwan,
Matthew J.

Shun-Shin, Kevin M. Alexander, Joseph Ebinger, Matthew P. Lungren,
Florian Rader, David H. Liang, Ingela Schnittger, Euan A. Ashley,
James Y. Zou, Jignesh Patel, Ronald Witteles, Susan Cheng, David
Ouyang. High- Throughput Precision Phenotyping of Left Ventricular
Hypertrophy With Cardiovascular Deep Learning. JAMA Cardiology,
2022; DOI: 10.1001/ jamacardio.2021.6059 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/02/220223133501.htm

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