Choroidal Vascular Fingerprints From Indocyanine Green Angiography Unveil Chorioretinal Disease State.

ABSTRACT:

Purpose

To develop an annotation-efficient deep learning algorithm for extracting multi-dimensional features of choroidal vasculature on indocyanine green angiography (ICGA) images via a human-in-the-loop (HITL) strategy and explore their relationship with multiple chorioretinal diseases.

Methods

The segmentation model was trained on a multi-source dataset that included both 55° ICGA and 200° ultra-widefield ICGA (UWF-ICGA) images, using a HITL strategy. Choroidal vascular fingerprints were generated from the segmentation maps, quantifying diameter, density, complexity, tortuosity, and branching angle. Reliability was assessed using intraclass correlation coefficients (ICC), and normal ranges for each measurement were estimated. The study retrospectively included 243 eyes di

SUBMITTER: Chen R 

PROVIDER: S-EPMC12510395 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature