The majority of patients in all the groups were of Han Chinese ethnicity. 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. The cheek, forehead and nose contributed more information to the algorithm than other facial areas. These include the low specificity of the test, that the test needs to be improved and validated in larger populations, and that it raises ethical questions about "misuse of information for discriminatory purposes. This information was used to create, train and validate the deep learning algorithm. 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).The new study was published in the European Heart Journal. Such fears have already been expressed over misuse of genetic data, and should be extensively revisited regarding the use of AI in medicine.Professor Zheng, 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. They also interviewed the patients to collect data on socioeconomic status, lifestyle and medical history. Unwanted dissemination of sensitive health record data, that can easily be extracted from a facial photo, renders technologies such as that discussed here a significant threat to personal data protection, potentially affecting insurance options.Professor Ji said: "The algorithm had 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. Radiologists reviewed the patients angiograms and assessed the degree of heart disease depending on how many blood vessels were narrowed by 50 per cent or more (>= 50 per cent stenosis), and their location. However, they are difficult for humans to use successfully to predict and quantify heart disease risk. lies in the fact that their deep learning algorithm requires simply a facial image as the sole data input, rendering it highly and easily applicable at large scale. Professor Zheng said: "Ethical issues in developing and applying these novel technologies is of key importance.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, who could be referred for further clinical investigations.
Indeed, the high risk individuals could have a CCTA, which would allow reliable risk stratification with the use of the new, AI-powered methodologies for CCTA image analysis. However, the algorithm requires further refinement and external validation in other populations and ethnicities. However, we need to improve the specificity as a false positive rate of as much as 46 per cent may cause anxiety and inconvenience to patients, as well as potentially overloading clinics with patients requiring unnecessary tests.Trained research nurses took four facial photos with digital cameras: one frontal, two profiles and one view of the top of the head."They continue: "Using selfies as a screening method can enable a simple yet efficient way to filter the general population towards more comprehensive clinical evaluation. highlights a new potential in medical diagnostics.".."To our knowledge, this is the first Wholesale Fuel filter OEM Factory work demonstrating that artificial intelligence can be used to analyse faces to detect heart disease. In the test group, the sensitivity was 80 per cent and specificity was 54 per cent.."It is known already that certain facial features are associated with an increased risk of heart disease. The study is the first to show that its possible to use a deep learning computer algorithm to detect coronary artery disease (CAD) by analysing four photographs of a persons face.They found that the algorithm outperformed existing methods of predicting heart disease risk (Diamond-Forrester model and the CAD consortium clinical score). 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, Peoples Republic of China."Professor Antoniades and Dr Kotanidis also write in their editorial that defining CAD as >= 50 per cent stenosis in one major coronary artery "may be a simplistic and rather crude classification as it pools in the non-CAD group individuals that are truly healthy, but also people who have already developed the disease but are still at early stages (which might explain the low specificity observed). We believe that future research on clinical tools should pay attention to the privacy, insurance and other social implications to ensure that the tool is used only for medical purposes.
