Deep survival modeling of longitudinal retinal OCT volumes for predicting the onset of atrophy in patients with intermediate AMD

In patients with age-related macular degeneration (AMD), the risk of progression to late stages is highly heterogeneous, and the prognostic imaging biomarkers remain unclear. We propose a deep survival model to predict the progression towards the late atrophic stage of AMD. The model combines the ad...

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Bibliographic Details
Published in:Biomedical optics express 2023-06, Vol.14 (6), p.2449-2464
Main Authors: Rivail, Antoine, Vogl, Wolf-Dieter, Riedl, Sophie, Grechenig, Christoph, Coulibaly, Leonard M, Reiter, Gregor S, Guymer, Robyn H, Wu, Zhichao, Schmidt-Erfurth, Ursula, Bogunović, Hrvoje
Format: Article
Language:English
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Summary:In patients with age-related macular degeneration (AMD), the risk of progression to late stages is highly heterogeneous, and the prognostic imaging biomarkers remain unclear. We propose a deep survival model to predict the progression towards the late atrophic stage of AMD. The model combines the advantages of survival modelling, accounting for time-to-event and censoring, and the advantages of deep learning, generating prediction from raw 3D OCT scans, without the need for extracting a predefined set of quantitative biomarkers. We demonstrate, in an extensive set of evaluations, based on two large longitudinal datasets with 231 eyes from 121 patients for internal evaluation, and 280 eyes from 140 patients for the external evaluation, that this model improves the risk estimation performance over standard deep learning classification models.
ISSN:2156-7085
2156-7085
DOI:10.1364/BOE.487206