1. Articles from Stefan Maetschke

    1-2 of 2
    1. Inference of visual field test performance from OCT volumes using deep learning

      Inference of visual field test performance from OCT volumes using deep learning

      Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted regularly to monitor disease progression. Here we address the question to what degree aggregate VFT measurements such as Visual Field Index (VFI) and Mean Deviation (MD) can be inferred from Optical Coherence Tomography (OCT) scans of the Optic Nerve Head (ONH) or the macula. Accurate inference of VFT measurements from OCT could reduce examination time and cost. We propose a novel 3D Convolutional Neural Network (CNN) for this task and compare its accuracy with classical machine learning (ML) algorithms trained on common, segmentation-based OCT, features employed for glaucoma diagnostics ...

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    2. A feature agnostic approach for glaucoma detection in OCT volumes

      A feature agnostic approach for glaucoma detection in OCT volumes

      Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based ...

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    1-2 of 2
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    1. (2 articles) NYU Langone Medical Center
    2. (2 articles) Hiroshi Ishikawa
    3. (2 articles) Gadi Wollstein
    4. (2 articles) Joel S. Schuman
    5. (1 articles) Gentuity
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