How Cardiovascular Risks are Associated with Retinal Disease
Along with the study of evaluating diabetic retinopathy by applying deep learning to retinal fundus image, predicting cardiovascular risk factor also became focused in medical field.
As identified retinal fundus images can display distinctive features of optic disc or blood vessels, correlation between heart diseases and retinal disease, it allowed to predict the cardiovascular risk factors via deep learning.
Other parameters like age, gender, blood pressure, body mass index, glucose, and cholesterol levels can critically impact different phenotype of retinal images and suggest additional signals of the risk.
Every additional signals can be rapidly derived from various retinal images via spending cheap price (Very Efficient).
Ophthalmologists used the methods of highlighting different anatomical location of retinal by markers to identify and predict the risk factors. Blood vessels were highlighted to predict risk factors such as age, smoking, and SBP.
To predict HbA1c, perivascular surroundings were highlighted, and for gender prediction, optic disc was primarily highlighted.
For other predictions, such as diastolic blood pressure and BMI, the circular border of the image was highlighted and suggested that the signals will be distributed more throughout the image.