Deep Learning Model for Accurate Automatic Determination of Phakic Status in Pediatric and Adult Ultrasound Biomicroscopy Images.

Journal: Translational vision science & technology
Published Date: Dec 23, 2020

Abstract

PURPOSE: Ultrasound biomicroscopy (UBM) is a noninvasive method for assessing anterior segment anatomy. Previous studies were prone to intergrader variability, lacked assessment of the lens-iris diaphragm, and excluded pediatric subjects. Lens status classification is an objective task applicable in pediatric and adult populations. We developed and validated a neural network to classify lens status from UBM images.

Authors

  • Christopher Le
    School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.
  • Mariana Baroni
    Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Alfred Vinnett
    Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Moran R Levin
    Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
  • Camilo Martinez
    Department of Ophthalmology, Children's National Medical System, Washington, DC, USA.
  • Mohamad Jaafar
    Department of Ophthalmology, Children's National Medical System, Washington, DC, USA.
  • William P Madigan
    Department of Ophthalmology, Children's National Medical System, Washington, DC, USA.
  • Janet L Alexander
    Department of Ophthalmology and Visual Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.

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