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

Journal: Translational vision science & technology
Published Date: Oct 3, 2022

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.

Authors

  • Tharindu De Silva
    Unit on Clinical Investigation of Retinal Disease, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
  • Kristina Hess
    Unit on Clinical Investigation of Retinal Disease, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
  • Peyton Grisso
    Unit on Clinical Investigation of Retinal Disease, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
  • Alisa T Thavikulwat
    Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Henry Wiley
    Division of Epidemiology & Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
  • Tiarnan D L Keenan
    Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
  • Emily Y Chew
    National Eye Institute, National Institutes of Health, Bethesda, Maryland. Electronic address: echew@nei.nih.gov.
  • Brett G Jeffrey
    Ophthalmic Genetics and Visual Function Branch, National Eye Institute, National Institutes of Health, Bethesda, MD, USA.
  • Catherine A Cukras
    Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA.

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