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Ursula Schmidt-Erfurth

Researcher at Medical University of Vienna

Publications -  703
Citations -  34745

Ursula Schmidt-Erfurth is an academic researcher from Medical University of Vienna. The author has contributed to research in topics: Macular degeneration & Optical coherence tomography. The author has an hindex of 82, co-authored 638 publications receiving 28143 citations. Previous affiliations of Ursula Schmidt-Erfurth include University of Vienna & Yahoo!.

Papers
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Response of Retinal Sensitivity to Intravitreal Anti-angiogenic Bevacizumab and Triamcinolone Acetonide for Patients with Diabetic Macular Edema over One Year.

TL;DR: Central macular function as measured by microperimetry in patients with acute DME improved in addition to anatomical restoration after intravitreal bevacizumab and triamcinolone injection during a follow-up of 1 year after treatment.
Journal Article

OCT biomarkers predictive for visual acuity in patients with diabetic macular edema

TL;DR: Baseline VA correlates best with ELM and IS-OS integrity and SRF presence and Automated analysis of ELM/IS-OS might allow VA prediction as their disruption is directly related to irreversible photoreceptor destruction and VA loss.
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Morphologic and Microvascular Differences Between Macular Neovascularization With and Without Subretinal Fibrosis.

TL;DR: In this paper, the authors evaluated morphologic and microvascular differences between eyes with and without subretinal fibrosis caused by neovascular age-related macular degeneration (nAMD).
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Three-dimensional composition of the photoreceptor cone layers in healthy eyes using adaptive-optics optical coherence tomography (AO-OCT).

TL;DR: In this paper, the authors used adaptive optics optical coherence tomography (AO-OCT) to assess the signal composition of cone photoreceptors in healthy retinas.
Posted Content

On orthogonal projections for dimension reduction and applications in variational loss functions for learning problems

TL;DR: This work investigates the relations between two standard objectives in dimension reduction, maximizing variance and preservation of pairwise relative distances and introduces new variational loss functions that enable integration of additional information via transformations and projections of the target data.