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Tharindu De Silva, Kristina Hess, Peyton Grisso, Alisa T. Thavikulwat, Henry Wiley, Tiarnan D. L. Keenan, Emily Y. Chew, Brett G. Jeffrey, Catherine A. Cukras; Deep Learning-Based Modeling of the Dark Adaptation Curve for Robust Parameter Estimation. Trans. Vis. Sci. Tech. 2022;11(10):40. https://doi.org/10.1167/tvst.11.10.40.
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This study investigates deep-learning (DL) sequence modeling techniques to reliably fit dark adaptation (DA) curves and estimate their key parameters in patients with age-related macular degeneration (AMD) to improve robustness and curve predictions.
A long-short-term memory autoencoder was used as the DL method to model the DA curve. The performance was compared against the classical nonlinear regression method using goodness-of-fit and repeatability metrics. Experiments were performed to predict the latter portion of the curve using data from early measurements. The prediction accuracy was quantified as the rod intercept time (RIT) prediction error between predicted and actual curves.
The two models had comparable goodness-of-fit measures, with root mean squared error (RMSE; SD) = 0.11 (0.04) log-units (LU) for the classical model and RMSE = 0.13 (0.06) LU for the DL model. Repeatability of the curve fits evaluated after introduction of random perturbations, and after performing repeated testing, demonstrated superiority of the DL method, especially among parameters related to cone decay. The DL method exhibited superior ability to predict the curve and RIT using points prior to −2 LU, with 3.1 ± 3.1 minutes RIT prediction error, compared to 19.1 ± 18.6 minutes RIT error for the classical method.
The parameters obtained from the DL method demonstrated superior robustness as well as predictability of the curve. These could provide important advances in using multiple DA curve parameters to characterize AMD severity.
Dark adaptation is an important functional measure in studies of AMD and curve modeling using DL methods can lead to improved clinical trial end points.
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