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 Leeds University, 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.
Writing in Nature Machine Intelligence, the researchers report in their paper - Predicting Infarction through your Retinal Scans and Minimal Personal Information - that the AI system had an accuracy of between 70 per cent and 80 per cent and could be used as a second referral mechanism for in-depth cardiovascular investigation.
Professor Alex Frangi, who holds the Diamond Jubilee Chair in Computational Medicine at Leeds University 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 early signs of heart disease.”
During the deep learning process, the AI system analysed the retinal scans and cardiac scans from over 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 following 12 months.
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 MRI of the heart. Those diagnostic tests can be expensive and 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 Leeds University and one of the authors, said: “The AI system is an excellent tool for unravelling the complex patterns that exist in nature, and that is what we have found – the intricate pattern of changes in the retina linked to changes in the heart.”
The study involved a worldwide collaboration of scientists, engineers and clinicians from Leeds University; Leeds Teaching Hospitals’ NHS Trust; York University; 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.
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