Prediction of White Matter Hyperintensity in Brain MRI Using Fundus Photographs via Deep Learning.

ABSTRACT:

Purpose

We investigated whether a deep learning algorithm applied to retinal fundoscopic images could predict cerebral white matter hyperintensity (WMH), as represented by a modified Fazekas scale (FS), on brain magnetic resonance imaging (MRI).

Methods

Participants who had undergone brain MRI and health-screening fundus photography at Hallym University Sacred Heart Hospital between 2010 and 2020 were consecutively included. The subjects were divided based on the presence of WMH, then classified into three groups according to the FS grade (0 vs. 1 vs. 2+) using age matching. Two pre-trained convolutional neural networks were fine-tuned and evaluated for prediction performance using 10-fold cross-validation.

Results

A total of 3726 fundus photographs from 1892 subjects

SUBMITTER: Cho BJ 

PROVIDER: S-EPMC9224833 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature