Selfies could help detect heart disease

Research lead Professor Zhe Zheng, from China’s National Centre for Cardiovascular Diseases, said such a screening tool could be a “cheap, simple and effective” way of identifying patients who need further investigation.

“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,” he said.

“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.

“However, the algorithm requires further refinement and external validation in other populations and ethnicities.”

Overall, the algorithm correctly detected heart disease in 80 per cent of cases – exactly the same as found in the group which was tested. 

And it correctly detected that heart disease was not present in 61 per cent of cases – a result which was marginally better than the 54 per cent rate found in tests. 

Co-researcher Professor Xiang-Yang Ji, added: “The algorithm had a moderate performance, and additional clinical information did not improve its performance, which means it could be used easily to predict potential heart disease based on facial photos alone.

“The cheek, forehead and nose contributed more information to the algorithm than other facial areas.”

But he said improvements needed to be made to reduce the rates of false positives, before such screening was rolled out more widely.

Charalambos Antoniades, Professor of Cardiovascular Medicine at the University of Oxford, said the simplicity of the approach meant it could be used at large scale, to screen the population, and identify those required further checks.

In an editorial co-written with Dr Christos Kotanidis, a DPhil student, he wrote:  “Using selfies as a screening method can enable a simple yet efficient way to filter the general population towards more comprehensive clinical evaluation.”

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