Retinal Arteriovenous Information Improves the Prediction Accuracy of Deep Learning-Based baPWV Index From Color Fundus Photographs.

ABSTRACT:

Purpose

To compare the prediction accuracy of brachial-ankle pulse wave velocity (baPWV) from color fundus photographs (CFPs) using different deep learning models.

Methods

This retrospective study analyzed the data of 696 participants whose baPWVs and CFPs were obtained during medical checkups. Arteriolar and venular probability maps, which were automatically calculated from the CFPs based on our modified deep U-net, Hokkaido University retinal vessel segmentation (HURVS) model, were applied as channel attention to retinal vessel location information to predict baPWV. The baPWV prediction parameters consisted of predicted baPWVs from a single-input model using CFPs only and from a three-input model using CFPs, and arteriolar and venular probability maps. The single- and thre

SUBMITTER: Saito M 

PROVIDER: S-EPMC11875032 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature