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Hrvoje Bogunović, Alessio Montuoro, Magdalena Baratsits, Maria G. Karantonis, Sebastian M. Waldstein, Ferdinand Schlanitz, Ursula Schmidt-Erfurth; Machine Learning of the Progression of Intermediate Age-Related Macular Degeneration Based on OCT Imaging. Invest. Ophthalmol. Vis. Sci. 2017;58(6):BIO141-BIO150. doi: https://doi.org/10.1167/iovs.17-21789.
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© ARVO (1962-2015); The Authors (2016-present)
To develop a data-driven interpretable predictive model of incoming drusen regression as a sign of disease activity and identify optical coherence tomography (OCT) biomarkers associated with its risk in intermediate age-related macular degeneration (AMD).
Patients with AMD were observed every 3 months, using Spectralis OCT imaging, for a minimum duration of 12 months and up to a period of 60 months. Segmentation of drusen and the overlying layers was obtained using a graph-theoretic method, and the hyperreflective foci were segmented using a voxel classification method. Automated image analysis steps were then applied to identify and characterize individual drusen at baseline, and their development was monitored at every follow-up visit. Finally, a machine learning method based on a sparse Cox proportional hazard regression was developed to estimate a risk score and predict the incoming regression of individual drusen.
The predictive model was trained and evaluated on a longitudinal dataset of 61 eyes from 38 patients using cross-validation. The mean follow-up time was 37.8 ± 13.8 months. A total of 944 drusen were identified at baseline, out of which 249 (26%) regressed during follow-up. The prediction performance was evaluated as area under the curve (AUC) for different time periods. Prediction within the first 2 years achieved an AUC of 0.75.
The predictive model proposed in this study represents a promising step toward image-guided prediction of AMD progression. Machine learning is expected to accelerate and contribute to the development of new therapeutics that delay the progression of AMD.
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