Analyzing four photographs of a person's face from selfies could be a cheap and simple way of detecting heart disease, according to a new study.
The study, published in the European Heart Journal, is the first to show that it is possible to use a deep learning computer algorithm to detect coronary artery disease (CAD).
Dr. Gaurav Minocha, Associate Director-Cardiology at Max Hospital, Vaishali informs that " 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, who could be referred for further clinical investigations.
"To our knowledge, this is the first work demonstrating that artificial intelligence can be used to analyze faces to detect heart disease. It is a step towards the development of a deep learning-based 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," said 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 and Peking Union Medical College, Beijing.
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.
Dr. Minocha opines that there is still more research required to validate the use of facial features in the diagnosis of heart disease. But it is a welcome step in the development of tools for the assessment of heart disease. This could be a cheap, simple and effective of identifying patients who need further investigation