Computational prediction of visual function and treatment effect in retinal diseases

The proposed methodology is an important step towards image-analysis
based individualization of patient management and treatment in one of the most cost-intensive fields of modern medicine. We propose to
identify and quantify retinal morphology using deep learning algorithms
such as convolutional neural networks (CNN) and to predict future
disease progression patterns and treatment response in patients based on spatio-temporal signatures.

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