1. End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging

    End-to-end deep learning model for predicting treatment requirements in neovascular AMD from longitudinal retinal OCT imaging

    Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but interindividual treatment requirements are vastly heterogeneous and currently poorly plannable resulting in suboptimal treatment frequency. Optical coherence tomography (OCT) with its 3D high-resolution imaging serves as a companion diagnostic to anti-VEGF therapy. This creates a need for building predictive models using automated image analysis of OCT scans acquired during the treatment initiation phase. We propose such a model based on deep learning (DL) architecture, comprised of a densely connected neural network (DenseNet) and a recurrent neural network (RNN), trainable end-to-end. The method starts by sampling several 2D-images ...

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