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, 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 make predictions. 

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 revolutionize 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 revolutionizing 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 analyzed the retinal scans and cardiac scans of 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 predict 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 unraveling 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.” 

In a new study, Norwegian researchers unveil a new relationship for climate models linking both carbon and heat uptake with the water-column stability in the Southern Ocean – also called the Antarctic Ocean. 

This climate model projections study is conducted by researchers from NORCE Norwegian Research Centre, and the Bjerknes Centre for Climate Research in Bergen, Norway. The Ocean is a powerful mitigator of climate change. It absorbs about 25 percent of the CO2 emitted by humans into the atmosphere (anthropogenic carbon) and 90 percent of the excess heat caused by global warming. One of the hotspots of anthropogenic carbon and excess heat uptake in the Southern Ocean. 

In this region, a key mechanism called “ocean subduction”, particularly located between 30°S and 55°S permits to efficient transfer of anthropogenic carbon and excess heat from the surface to the depths of the ocean, where it can be stored for centuries. Climate model projections of these carbon and heat sinks remain highly uncertain. Reducing such uncertainties is required to effectively guide the development of climate mitigation policies for meeting ambitious climate targets.

- Climate models have significantly improved on many aspects during the last decades, yet they still show some biases that we must reduce, says lead author Timothée Bourgeois. Models with a more unstable water column have an efficient subduction mechanism and a strengthened carbon and heat uptake in our region of interest, lead author Bourgeois and his colleagues Nadine Goris, Jörg Schwinger , and Jerry F. Tjiputra point out. On the contrary, models with a stable or “stratified” water column show reduced subduction and reduced uptakes. Using a powerful and recent statistical methodology as well as observational data describing today’s water-column stability, the new relationship permits to reduce the uncertainty of future estimates of the anthropogenic carbon uptake by up to 53 percent and the excess heat uptake by 28 percent. These results highlight that, for this region, an improved representation of the water-column stability in climate models is key to improving climate change projections.