Korea Biomedical Review | July 15, 2021
The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and hematological parameters. However, the broader applicability of retinal photograph-based deep learning for predicting other systemic biomarkers and the generalisability of this approach to various populations remains unexplored. With use of 236 257 retinal photographs from seven diverse Asian and European cohorts (two health screening centres in South Korea, the Beijing Eye Study, three cohorts in the Singapore Epidemiology of Eye Diseases study, and the UK Biobank), we evaluated the capacities of 47 deep-learning algorithms to predict 47 systemic biomarkers as outcome variables, including demographic factors (age and sex); body composition measurements; blood pressure; haematological parameters; lipid profiles; biochemical measures; biomarkers related to liver function, thyroid function, kidney function, and inflammation; and diabetes. The standard neural network architecture of VGG16 was adopted for model development.
A joint research team led by Severance Hospital professors has developed an algorithm that predicts the risk of new cardiovascular diseases by observing changes in blood vessels in the retina images, the hospital said in a news release Thursday.
A joint research team led by Severance Hospital has developed an algorithm that predicts the risk of new cardiovascular diseases by observing changes in blood vessels in the retina images.
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