How Cardiovascular Risks are Associated with Retinal Disease

/ / All, Digital Health

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.

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