Deep Learning-Based Modeling of the Dark Adaptation Curve for Robust Parameter Estimation.

ABSTRACT:

Purpose

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.

Methods

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.

Results

The two models had comparable goodness-of-fit measures, wi

SUBMITTER: De Silva T 

PROVIDER: S-EPMC9631495 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature