Selfie sent to doctor could detect heart disease, say experts
Sending a selfie to the doctor could be a cheap and simple way of detecting heart disease, according to researchers.
They claim their study, published in the European Heart Journal, is the first to show it’s possible to use a deep learning computer algorithm to detect coronary artery disease (CAD) by analysing four photographs of a person’s face.
Although the algorithm needs to be developed further and tested in larger groups of people from different ethnic backgrounds, the researchers say it has the potential to be used as a screening tool that could identify possible heart disease in people in the general population or in high-risk groups.
It is known already that certain facial features are associated with an increased risk of heart disease. These include thinning or grey hair, wrinkles, ear lobe crease, xanthelasmata – small, yellow deposits of cholesterol underneath the skin, usually around the eyelids and arcus corneae (fat and cholesterol deposits that appear as a hazy white, grey or blue opaque ring in the outer edges of the cornea).
However, they are difficult for humans to use successfully to predict and quantify heart disease risk.
Professor Zhe Zheng, who led the research and is vice director of the National Center for Cardiovascular Diseases and vice president of Fuwai Hospital, Chinese Academy of Medical Sciences, said: “To our knowledge, this is the first work demonstrating that artificial intelligence can be used to analyse faces to detect heart disease.
“It is a step towards the development of a deep learningbased tool that could be used to assess the risk of heart disease, either in outpatient clinics or by means of patients taking ‘selfies’ to perform their own screening. This could guide further diagnostic testing or a clinical visit.”
He added: “Our ultimate goal is to develop a self-reported application for high-risk communities to assess heart disease risk in advance of visiting a clinic.
“This could be a cheap, simple and effective of identifying patients who need further investigation.
“However, the algorithm requires further refinement and external validation in other populations and ethnicities.”
Prof Zheng and Professor Xiang-yang Ji, who is director of the Brain and Cognition Institute in the Department of Automation at Tsinghua University, Beijing, and other colleagues enrolled 5,796 patients from eight hospitals in China to the study between July 2017 and March 2019.