Beijing: Posting a selfie to the doctor could be a simple way of detecting heart disease, say, researchers, claiming that it is possible for a computer algorithm to detect coronary artery disease by analyzing pictures of a person’s face.
According to the study, published in the European Heart Journal, the algorithm has the potential to be used as a screening tool that could identify possible heart disease in people in the general population.
“To our knowledge, this is the first work demonstrating that artificial intelligence can be used to analyze faces to detect heart disease,” said study author Zhe Zheng from Peking Union Medical College in China.
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”.
For the findings, the research team enrolled 5,796 patients from eight hospitals in China to the study between July 2017 and March 2019.
The patients were undergoing imaging procedures to investigate their blood vessels, such as coronary angiography or coronary computed tomography angiography (CCTA).
They were divided randomly into training (5,216 patients, 90 percent) or validation (580, 10 percent) groups.
Trained research nurses took four facial photos with digital cameras: one frontal, two profiles and one view of the top of the head. They also interviewed the patients to collect data on socioeconomic status, lifestyle and medical history.
Radiologists reviewed the patients’ angiograms and assessed the degree of heart disease depending on how many blood vessels were narrowed by 50 percent or more and their location.
This information was used to create, train and validate the deep learning algorithm.
The researchers then tested the algorithm on further 1,013 patients from nine hospitals in China, enrolled between April 2019 and July 2019. The majority of patients in all the groups were of Han Chinese ethnicity.
They found that the algorithm outperformed existing methods of predicting heart disease risk.
In the validation group of patients, the algorithm correctly detected heart disease in 80 percent of cases and correctly detected heart disease was not present in 61 percent of cases.
“However, we need to improve the specificity as a false positive rate of as much as 46 percent may cause anxiety and inconvenience to patients, as well as potentially overloading clinics with patients requiring unnecessary tests,” the authors wrote.