Skip to content

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted.
The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit cc.nih.gov.
Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

AI can identify heart disease from an eye scan

January 25, 2022
Artificial Intelligence Imaging Retina
Translational Research
Grantee
Data Science and Health Informatics Division of Epidemiology and Clinical Application

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.

Image of a heart superimposed over a retinal image

A graphic representation of the idea of using a scan of the eye to get a window into heart health. Credit: University of Leeds.

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 an 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 in their paper 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 examination.

The study involved a collaboration of scientists, engineers, and clinicians from around the world, including the National Eye Institute.

 

Diaz-Pinto, A. et al. Predicting myocardial infarction through retinal scans and minimal personal information. Nature Machine Intelligence, doi:10.1038/s42256-021-00427-7 (2022).