Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging

Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multimodal Imaging

To develop deep learning models for annualized geographic atrophy (GA) growth rate prediction using fundus autofluorescence (FAF) images and spectral-domain OCT volumes from baseline visits, which can be used for prognostic covariate adjustment to increase power of clinical trials.This retrospective...

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Hlavní autoři: Neha Anegondi, Simon S. Gao, Verena Steffen, Richard F. Spaide, Srinivas R. Sadda, Frank G. Holz, Christina Rabe, Lee Honigberg, Elizabeth M. Newton, Julia Cluceru, Michael Kawczynski, Thomas Bengtsson, Daniela Ferrara, Qi Yang
Médium: Artigo
Jazyk:angličtina
Vydáno: 2022
On-line přístup:https://doi.org/10.1016/j.oret.2022.08.018
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https://doi.org/10.1016/j.oret.2022.08.018

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