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

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.

Methods

Two hundred eighty-five UBM images were collected in the Pediatric Anterior Segment Imaging Innovation Study (PASIIS) from 80 eyes of 51 pediatric and adult subjects (median age = 4.6 years, range = 3 weeks to 90 years) with lens status phakic, aphakic, or pseudophakic (n = 33, 7, and 21 subjects, respectively). Following transfer learning, a pretrai

SUBMITTER: Le C 

PROVIDER: S-EPMC7779873 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature