YSM Issue 96.3

FOCUS
Artificial Intelligence
DEEP
LEARNING
An Unexpected
Tool To Fight
Heart Valve Disease
BY SOPHIA BURICK
PHOTOGRAPH COURTESY OF CAROLINE BUCKY
Severe aortic stenosis (AS) is a common form of valvular heart
disease that involves the aortic valve becoming unusually
narrow, affecting five percent of people above the age of
sixty-five. Early diagnosis is essential to successful intervention.
Usually, AS is detected through Doppler echocardiography, or
ultrasound imaging of the heart. However, performing Doppler
echocardiography requires access to specialized equipment as well
as professionals who know how to operate the equipment and
interpret the results. This discrepancy between the large population
of individuals at risk for AS and the small amount of resources
available for its diagnosis makes it difficult to achieve early diagnosis
of AS, negatively impacting patient outcomes.
Researchers at the Cardiovascular Data Science (CarDS) Lab at
Yale recently published in European Heart Journal a creative new
approach to making AS diagnostic tools more accessible—combining
deep learning with simple ultrasound scans. Handheld devices that
use ultrasound imaging to visualize the heart are much more widely
available than the equipment necessary for Doppler echocardiography,
but the images and videos alone produced by these ultrasound scans
are difficult to use to diagnose AS. “Patients are often not seen by a
cardiologist until they are very late in their disease stage,” Evangelos
Oikonomou, a postdoctoral fellow in the CarDS Lab, said. “There’s a big
opportunity to diagnose the disease earlier in this patient population.”
The researchers at the CarDS Lab developed a novel deep learning
model that is capable of using 2D echocardiograms, which are produced
by simple ultrasound imaging, to identify AS without specialized
Doppler equipment. Deep learning is a kind of machine learning
that employs computer networks built to resemble human neural
networks—in short, it teaches computers how to learn like humans.
“You train the algorithm by showing it multiple different images
and giving feedback to the algorithm as to whether its prediction
[about what the image is] is correct or wrong,” Oikonomou said.
“What the algorithm does is every time it gets [its prediction] wrong,
it tries to adjust its approach and learn something from its errors.”
These deep learning algorithms are often more perceptive to patterns
than humans, allowing them to reach conclusions that might not be
apparent to a doctor trying to interpret ultrasound images. “That’s
where the performance of an AI algorithm may actually exceed that of
a human operator,” Oikonomou said.
To develop their algorithm, the researchers needed to train it to be
able to recognize severe AS. To do this, they sourced a massive amount
of 2D cardiac ultrasound videos from patients in the Yale New Haven
Health system with no AS, non-severe AS, and severe AS. Using this
dataset, the algorithm learned how to identify specific phenomena
in the videos associated with each class of AS diagnosis. Once the
researchers trained the algorithm to learn what to look for, they had
to validate that the algorithm was truly capable of differentiating
non-AS, non-severe AS, and severe AS ultrasound videos. To prove
the algorithm’s success, they had it sort a new dataset from different
patients in New England and California. The deep learning algorithm
proved highly accurate in sorting the videos across all patient datasets.
The researchers’ vision is that their algorithm can be used by any
medical provider with a simple ultrasound scanner to catch AS early.
This removes the existing barriers to AS diagnosis, like specialized
Doppler echocardiography equipment and the training of medical
providers to accurately interpret results, making AS diagnoses more
accessible to patients and simpler for providers. If the algorithm
is widely used, it could be a major step forward for successful AS
intervention. “Hopefully, we can make this as cost-efficient as possible,”
Oikonomou said. “It’s very easy to do—it takes two or three minutes,
and people can probably be screened once in their lifetime.”
Beyond its immediate impact in improving outcomes for AS patients,
this deep learning algorithm reveals the broader potential of applying
cutting-edge computer science to healthcare. “I think this could be
applied to other things such as hypertrophic cardiomyopathy, which
is a genetic heart condition that is very common but most people don’t
ever get diagnosed,” Oikonomou said.
With increasingly high patient burdens and medical staff stretched
thin, it’s inevitable that some patients will slip through the cracks of
the healthcare system. Machine and deep learning models could be
used across a variety of applications to identify diagnoses that are
sometimes missed by medical staff. The CarDS Lab’s algorithm is
proof of the great positive impact that computer science and artificial
intelligence stand to have on patient care and outcomes. ■
8 Yale Scientific Magazine September 2023 www.yalescientific.org