Using the eye as a window into heart disease

Date:
January 25, 2022
Source:
University of Leeds
Summary:
Scientists have developed an artificial intelligence (AI) system
that can analyze eye scans taken during a routine visit to an
optician or eye clinic and identify patients at a high risk of a
heart attack. Doctors have recognized that changes to the tiny blood
vessels in the retina are indicators of broader vascular disease,
including problems with the heart. In the research, deep learning
techniques were used to train an AI system to automatically read
retinal scans and identify those people who, over the following
year, were likely to have a heart attack.



FULL STORY ========================================================================== Scientists have developed an artificial intelligence (AI) system that
can analyse eye scans taken during a routine visit to an optician or
eye clinic and identify patients at a high risk of a heart attack.


========================================================================== Doctors have recognised that changes to the tiny blood vessels in the
retina are indicators of broader vascular disease, including problems
with the heart.

In the research, led by the University of Leeds, deep learning techniques
were used to train the AI system to automatically read retinal scans and identify those people who, over the following year, were likely to have
a heart attack.

Deep learning is a complex series of algorithms that enable computers
to identify patterns in data and to make predictions.

Writing in the journal Nature Machine Intelligence, the researchers
report that the AI system had an accuracy of between 70% and 80% and
could be used as a second referral mechanism for in-depth cardiovascular investigation.

The use of deep learning in the analysis of retinal scans could
revolutionise the way patients are regularly screened for signs of
heart disease.



========================================================================== Professor Alex Frangi, who holds the Diamond Jubilee Chair in
Computational Medicine at the University of Leeds and is a Turing
Fellow at the Alan Turing Institute, supervised the research. He said: "Cardiovascular diseases, including heart attacks, are the leading cause
of early death worldwide and the second-largest killer in the UK. This
causes chronic ill-health and misery worldwide.

"This technique opens-up the possibility of revolutionising the screening
of cardiac disease. Retinal scans are comparatively cheap and routinely
used in many optician practices. As a result of automated screening,
patients who are at high risk of becoming ill could be referred to
specialist cardiac services.

"The scans could also be used to track the early signs of heart disease."
The study involved a worldwide collaboration of scientists, engineers and clinicians from the University of Leeds; Leeds Teaching Hospitals' NHS
Trust; the University of York; the Cixi Institute of Biomedical Imaging
in Ningbo, part of the Chinese Academy of Sciences; the University of
Cote d'Azur, France; the National Centre for Biotechnology Information
and the National Eye Institute, both part of the National Institutes
for Health in the US; and KU Leuven in Belgium.

The UK Biobank provided data for the study.



========================================================================== Chris Gale, Professor of Cardiovascular Medicine at the University of
Leeds and a Consultant Cardiologist at Leeds Teaching Hospitals NHS Trust,
was one of the authors of the research paper.

He said: "The AI system has the potential to identify individuals
attending routine eye screening who are at higher future risk of
cardiovascular disease, whereby preventative treatments could be started earlier to prevent premature cardiovascular disease." Deep learning
During the deep learning process, the AI system analysed the retinal
scans and cardiac scans from more than 5,000 people. The AI system
identified associations between pathology in the retina and changes in
the patient's heart.

Once the image patterns were learned, the AI system could estimate the
size and pumping efficiency of the left ventricle, one of the heart's
four chambers, from retinal scans alone. An enlarged ventricle is linked
with an increased risk of heart disease.

With information on the estimated size of the left ventricle and its
pumping efficiency combined with basic demographic data about the patient, their age and sex, the AI system could make a prediction about their
risk of a heart attack over the subsequent 12 months.

Currently, details about the size and pumping efficiency of a patient's
left ventricle can only be determined if they have diagnostic tests such
as echocardiography or magnetic resonance imaging of the heart. Those diagnostic tests can be expensive and are often only available in a
hospital setting, making them inaccessible for people in countries with
less well-resourced healthcare systems -- or unnecessarily increasing healthcare costs and waiting times in developed countries.

Sven Plein, British Heart Foundation Professor of Cardiovascular
Imaging at the University of Leeds and one of the authors of the
research paper, said: "The AI system is an excellent tool for
unravelling the complex patterns that exist in nature, and that
is what we have found here -- the intricate pattern of changes in
the retina linked to changes in the heart." special promotion
Explore the latest scientific research on sleep and dreams
in this free online course from New Scientist -- Sign_up_now_>>> ========================================================================== Story Source: Materials provided by University_of_Leeds. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Andres Diaz-Pinto, Nishant Ravikumar, Rahman Attar, Avan
Suinesiaputra,
Yitian Zhao, Eylem Levelt, Erica Dall'Armellina, Marco Lorenzi,
Qingyu Chen, Tiarnan D. L. Keenan, Elvira Agro'n, Emily Y. Chew,
Zhiyong Lu, Chris P. Gale, Richard P. Gale, Sven Plein, Alejandro
F. Frangi.

Predicting myocardial infarction through retinal scans and minimal
personal information. Nature Machine Intelligence, 2022; DOI:
10.1038/ s42256-021-00427-7 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220125112548.htm

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