Heart disease diagnosis using selfies

Medical research has seen major advances during the past few years, particularly as big data have emerged in biomedical research. Implementation of AI technology in day-to-day clinical practice has already begun, with emerging applications using it to interpret medical images, read pathology slides, analyse electrocardiograms (ECGs), track vital signs, and many other uses.

Speedy diagnostic testing is rapidly becoming an important part of medical practice. Information extracted from analysis of an individual’s facial photo utilizing the proposed technology can unquestionably benefit the individual, the attending physician, and the healthcare system altogether. Early detection of individuals at risk for coronary artery disease (CAD) can initiate lifestyle and other personal mitigation approaches, guide medication treatment, and inspire a novel approach in diagnostic testing and screening algorithms for the general population. At the same time, such a technology may raise concerns about misuse of information for discriminatory purposes. 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.

The authors of a new paper published in August 2020, called “Feasibility of using deep learning to detect coronary artery disease based on facial photo”, deploy a large training set of 5216 individuals to develop their deep learning algorithm which is tested on a group of 1013 individuals predominantly of Han Chinese ethnicity recruited in tertiary centres across China. All patients underwent a standardized protocol for acquisition of facial images, and a coronary computed tomography angiography (CCTA) was used as the reference method for dichotomizing the cohort into groups of CAD presence. Facial appearance has long been identified as a marker of cardiovascular risk, with features such as male pattern baldness, earlobe crease, xanthelasmata, and skin wrinkling being the most common predictors. The robustness of this approach 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. Using selfies as a screening method can enable a simple yet efficient way to filter the general population towards more comprehensive clinical evaluation. Such an approach can also be highly relevant to regions of the globe that are underfunded and have weak screening programmes for cardiovascular disease.

There are still a few points, however, for consideration that make a practical application of the current algorithm challenging. The low specificity of the method raises a concern regarding false-positive results that may confuse both patient and clinician, and eventually overload the system with redundant and unnecessary testing. The photo pre-processing used may be another issue for consideration; resolution was reduced to 256 × 256 pixels, which hinders the detection of fine facial features, such as arcus lipoides, that may play a role in the diagnostic accuracy of the model. Moreover, proper external validation of deep learning models in populations that are independent is needed to ascertain their use and functionality. Finally, in an era that observes a record surge in cosmetic surgery, we should keep in mind that artificial facial alterations may severely discredit such screening tools.

Paper: Lin S , Li Z, Fu B, Chen S, Li X, Wang Y, Wang X, Lv B, Xu B, Song X, Zhang Y-J, Cheng X, Huang W, Pu J, Zhang Q, Xia Y, Du B, Ji X, Zheng Z. Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J 2020;doi:10.1093/eurheartj/ehaa640. 2020

Source – abridged and adapted