Deep Learning to Predict Geographic Atrophy Area and Growth Rate from Multi-modal Imaging (90 characters; max 135 characters).

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Abstract

To develop deep learning models for annualized geographic atrophy (GA) growth rate prediction using fundus autofluorescence (FAF) images and spectral-domain optical coherence tomography (OCT) volumes from baseline visits, which can be used for prognostic covariate adjustment to increase power of clinical trials.This retrospective analysis estimated GA growth rate as the slope of a linear fit on all available measurements of lesion area over a 2-year period. Three multi-task deep learning models-FAF-only, OCT-only, and multi-modal (FAF and OCT)-were developed to predict concurrent GA area and annualized growth rate.Patients were from prospective and observational lampalizumab clinical trials.The three models were trained on the development data set, tested on the holdout set and further evaluated on the independent test sets. Baseline FAF images and OCT volumes from study eyes of patients with bilateral GA (NCT02247479; NCT02247531; NCT02479386) were split into development (1279 patients/eyes) and holdout (443 patients/eyes) sets. Baseline FAF images from study eyes of NCT01229215 (106 patients/eyes) and NCT02399072 (169 patients/eyes) were used as independent test sets.Model performance was evaluated using squared Pearson correlation coefficient (r2) between observed and predicted lesion areas/growth rates. Confidence intervals (CIs) were calculated by bootstrap resampling (B = 10 000).On the holdout data set, r2 (95% CI) of the FAF-only, OCT-only, and multi-modal models for GA lesion area prediction was 0.96 (0.95-0.97), 0.91 (0.87-0.95), and 0.94 (0.92-0.96), respectively, and for GA growth rate prediction was 0.48 (0.41-0.55), 0.36 (0.29-0.43), and 0.47 (0.40-0.54), respectively. On the two independent test sets, r2 of the FAF-only model for GA lesion area was 0.98 (0.97-0.99) and 0.95 (0.93-0.96), and for GA growth rate was 0.65 (0.52-0.75) and 0.47 (0.34-0.60).We show the feasibility of using baseline FAF images and/or OCT volumes to predict individual GA area and growth rates using a multi-task deep learning approach. The deep learning- based growth rate predictions could be used for covariate adjustment to increase power of clinical trials.Copyright © 2022. Published by Elsevier Inc.