Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders.

ABSTRACT:

Purpose

To visualize and quantify structural patterns of optic nerve edema encountered in papilledema during treatment.

Methods

A novel bi-channel deep-learning variational autoencoder (biVAE) model was trained using 1498 optical coherence tomography (OCT) scans of 125 subjects over time from the Idiopathic Intracranial Hypertension Treatment Trial (IIHTT) and 791 OCT scans of 96 control subjects from the University of Iowa. An independent test dataset of 70 eyes from 70 papilledema subjects was used to evaluate the ability of the biVAE model to quantify and reconstruct the papilledema spatial patterns from input OCT scans using only two variables.

Results

The montage color maps of the retinal nerve fiber layer (RNFL) and total retinal thickness (TRT) produced by the

SUBMITTER: Wang JR 

PROVIDER: S-EPMC10795546 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature