1. Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images

    Predicting Macular Edema Recurrence from Spatio-Temporal Signatures in Optical Coherence Tomography Images

    Prediction of treatment responses from available data is key to optimizing personalized treatment. Retinal diseases are treated over long periods and patients’ response patterns differ substantially, ranging from a complete response to a recurrence of the disease and need for re-treatment at different intervals. Linking observable variables in high-dimensional observations to outcome is challenging. In this paper, we present and evaluate two different data-driven machine learning approaches operating in a high-dimensional feature space: sparse logistic regression and random forests-based extra trees (ET). Both identify spatio-temporal signatures based on retinal thickness features measured in longitudinal spectral-domain optical coherence tomography (OCT) imaging ...

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