'Selfies' could be used to detect heart disease
New research uses artificial intelligence to analyze facial photos

Date:
August 21, 2020
Source:
European Society of Cardiology
Summary:
Sending a 'selfie' to the doctor could be a cheap and simple way
of detecting heart disease, according to researchers. Their study
is the first to show that it's possible to use a deep learning
computer algorithm to detect coronary artery disease (CAD) by
analyzing four photographs of a person's face.



FULL STORY ========================================================================== Sending a "selfie" to the doctor could be a cheap and simple way
of detecting heart disease, according to the authors of a new study
published today (Friday) in the European Heart Journal.


==========================================================================
The study is the first to show that it's possible to use a deep learning computer algorithm to detect coronary artery disease (CAD) by analysing
four photographs of a person's face.

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.

"To our knowledge, this is the first work demonstrating that artificial intelligence can be used to analyse 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, People's Republic of China.

He continued: "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. 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." 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.



========================================================================== Prof. 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. 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%) or validation (580, 10%) 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% or more (>= 50% stenosis), and
their location. This information was used to create, train and validate
the deep learning algorithm.

The researchers then tested the algorithm on a 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 out-performed existing methods of predicting heart disease risk (Diamond-Forrester model and the CAD consortium
clinical score). In the validation group of patients, the algorithm
correctly detected heart disease in 80% of cases (the true positive rate
or 'sensitivity') and correctly detected heart disease was not present
in 61% of cases (the true negative rate or 'specificity'). In the test
group, the sensitivity was 80% and specificity was 54%.

Prof. Ji said: "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. However, we need to improve the specificity as a
false positive rate of as much as 46% may cause anxiety and inconvenience
to patients, as well as potentially overloading clinics with patients
requiring unnecessary tests." As well as requiring testing in other
ethnic groups, limitations of the study include the fact that only one
centre in the test group was different to those centres which provided
patients for developing the algorithm, which may further limit its generalisabilty to other populations.



==========================================================================
In an accompanying editorial, Charalambos Antoniades, Professor of Cardiovascular Medicine at the University of Oxford, UK, and Dr Christos Kotanidis, a DPhil student working under Prof. Antoniades at Oxford,
write: "Overall, the study by Lin et al. highlights a new potential in
medical diagnostics......The robustness of the approach of Lin et al. 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." 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. Such an approach can also be
highly relevant to regions of the globe that are underfunded and have weak screening programmes for cardiovascular disease. A selection process that
can be done as easily as taking a selfie will allow for a stratified flow
of people that are fed into healthcare systems for first-line diagnostic testing with CCTA. 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." They highlight some
of the limitations that Prof. Zheng and Prof. Ji also include in their
paper. 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. 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. Such fears have already been
expressed over misuse of genetic data, and should be extensively revisited regarding the use of AI in medicine." The authors of the research paper
agree on this point. Prof. Zheng said: "Ethical issues in developing and applying these novel technologies is of key importance. 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." Prof. Antoniades and Dr. Kotanidis also write
in their editorial that defining CAD as >= 50% 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)."

========================================================================== Story Source: Materials provided by European_Society_of_Cardiology. Note: Content may be edited for style and length.


========================================================================== Journal References:
1. Shen Lin, Zhigang Li, Bowen Fu, Sipeng Chen, Xi Li, Yang Wang,
Xiaoyi
Wang, Bin Lv, Bo Xu, Xiantao Song, Yao-Jun Zhang, Xiang Cheng,
Weijian Huang, Jun Pu, Qi Zhang, Yunlong Xia, Bai Du, Xiangyang Ji,
Zhe Zheng.

Feasibility of using deep learning to detect coronary artery
disease based on facial photo. European Heart Journal, 2020; DOI:
10.1093/ eurheartj/ehaa640
2. Christos P. Kotanidis, Charalambos Antoniades. Selfies in
cardiovascular
medicine: welcome to a new era of medical diagnostics. European
Heart Journal, 2020; DOI: 10.1093/eurheartj/ehaa608 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/08/200821103853.htm

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