Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.

Journal: Eye (London, England)

PMID: 37311835

Abstract

PURPOSE: To evaluate the reliability of automated fluid detection in identifying retinal fluid activity in OCT scans of patients treated with anti-VEGF therapy for neovascular age-related macular degeneration by correlating human expert and automated measurements with central retinal subfield thickness (CSFT) and fluid volume values.

Authors

Maximilian Pawloff

Departments of Ophthalmology, and.
Bianca S Gerendas

Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
Gabor Deak

Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.
Hrvoje Bogunović

Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
Anastasiia Gruber

Statistics, Medical University of Vienna, Vienna, Austria.
Ursula Schmidt-Erfurth

Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.

Keywords

Angiogenesis Inhibitors Deep Learning Humans Intravitreal Injections Reproducibility of Results Subretinal Fluid Tomography, Optical Coherence Vascular Endothelial Growth Factor A Visual Acuity Wet Macular Degeneration

External Resources

View on PubMed Access via DOI PubMed (37311835)

Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD.

Abstract

Authors

Keywords

External Resources

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