scispace - formally typeset
U

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
More filters
Journal ArticleDOI

Influence of lens opacities and cataract severity on quantitative fundus autofluorescence as a secondary outcome of a randomized clinical trial

Gregor Sebastian Reiter, +7 more
- 16 Jun 2021 - 
TL;DR: In this article, the impact of age-related lens opacities and advanced cataract, quantified by LOCS III grading, on quantitative autofluorescence (qAF) measurements in patients before and after Cataract surgery was investigated.
Book ChapterDOI

OCT fluid detection and quantification

Hrvoje Bogunovic, +3 more
TL;DR: This chapter summarizes recent work and provides an overview of the state of the art of machine and deep learning algorithms for detecting and segmenting retinal fluid on OCT, and demonstrates the benefits such automated methods bring in the form of two clinically relevant applications that are enabled by havingretinal fluid accurately quantified.
Journal ArticleDOI

Identification of microvascular and morphological alterations in eyes with central retinal non-perfusion.

Dorottya Hajdu, +7 more
- 10 Nov 2020 - 
TL;DR: This study has shown that the best predictor of visual outcome in center involved ischemic diseases is the size of FAZ, which was demonstrated when analyzing all patients with retinal ischemia.
Journal Article

Morphological parameters relevant for long-term outcomes during therapy of diabetic macular edema in the RESTORE Extension trial

Bianca S Gerendas, +7 more
- 11 Jun 2015 - 
Journal Article

Machine learning to predict the individual progression of AMD from imaging biomarkers

Ursula Schmidt-Erfurth, +8 more
- 23 Jun 2017 - 
TL;DR: A fully automated machine learning method is developed and validated predicting the conversion to advanced AMD on an individual basis, using the extracted imaging biomarkers as well as known genetic risk factors of AMD (34 single-nucleotide polymorphisms) as input features.