1. Linking Function and Structure with ReSenseNet: Predicting Retinal Sensitivity from Optical Coherence Tomography using Deep Learning

    Linking Function and Structure with ReSenseNet: Predicting Retinal Sensitivity from Optical Coherence Tomography using Deep Learning

    Purpose: Currently used measures of retinal function are limited by being subjective, non-localized and/or taxing for patients. To address these limitations, we sought to develop and evaluate a deep learning (DL) method to automatically predict a functional endpoint (retinal sensitivity) from structural optical coherence tomography (OCT) images. Design: Retrospective cross-sectional study. Subjects: In total, 714 volumes of 289 patients were used in this study. Methods: A novel deep learning algorithm was developed to automatically predict a comprehensive retinal sensitivity map from OCTs. 463 SD-OCT volumes from 174 patients and their corresponding microperimetry examinations (Nidek MP-1) were used for development ...

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