Forecasting Retinal Nerve Fiber Layer Thickness from Multimodal Temporal Data Incorporating OCT Volumes

Purpose: The purpose of this study was to develop a machine learning model to forecast future circumpapillary retinal nerve fiber layer (cpRNFL) thickness in eyes of healthy, glaucoma suspect, and glaucoma participants from multimodal temporal data. Design: Retrospective analysis of a longitudinal clinical cohort. Participants: Longitudinal clinical cohort of healthy, glaucoma suspect, and glaucoma participants. Methods: The forecasting models used multimodal patient information including clinical (age and intraocular pressure), structural (cpRNFL thickness derived from scans as well as deep learning-derived OCT image features), and functional (visual field test parameters) data and the intervisit interval for prediction of cpRNFL thickness at the next visit. Four models were developed based on the number of visits used (n = 1 to 4). Longitudinal data from 1089 participants (mean observation period, 3.65±1.73 years) was used with 80% of the cohort for the development of the models. The results of our models were compared with those of a commonly adopted linear regression model, which we refer to here as linear trend-based estimation (LTBE). Main Outcome Measures: The mean absolute difference and Pearson's correlation coefficient between the true and forecasted values of the cpRNFL in the healthy, glaucoma suspect, and glaucoma patients. Results: The best forecasting model of cpRNFL was obtained using 3 visits and incorporated deep learning-derived OCT image features. The mean error was 1.10±0.60 μm, 1.79±1.73 μm, and 1.87±1.85 μm in eyes of healthy, glaucoma suspect, and glaucoma participants, respectively. Our method significantly outperformed the LTBE model for glaucoma suspect and glaucoma participants (P < 0.001), which showed a mean error of 1.55±1.16 μm, 2.4±2.67 μm, and 3.02±3.06 μm in the 3 groups, respectively. The Pearson's correlation coefficient between the forecasted value and the measured thickness was ρ = 0.96 (P < 0.01), ρ = 0.95 (P < 0.01), and ρ = 0.96 (P < 0.01) for the 3 groups, respectively. Conclusions: The performance of the proposed forecasting model for cpRNFL is consistent across glaucoma suspect and glaucoma patients, which implies the robustness of the developed model against the disease state. These forecasted values may be useful to personalize patient care by determining the most appropriate intervisit schedule for timely interventions.