Automated Detection of Vascular Leakage in Fluorescein Angiography - A Proof of Concept.

ABSTRACT:

Purpose

The purpose of this paper was to develop a deep learning algorithm to detect retinal vascular leakage (leakage) in fluorescein angiography (FA) of patients with uveitis and use the trained algorithm to determine clinically notable leakage changes.

Methods

An algorithm was trained and tested to detect leakage on a set of 200 FA images (61 patients) and evaluated on a separate 50-image test set (21 patients). The ground truth was leakage segmentation by two clinicians. The Dice Similarity Coefficient (DSC) was used to measure concordance.

Results

During training, the algorithm achieved a best average DSC of 0.572 (95% confidence interval [CI] = 0.548-0.596). The trained algorithm achieved a DSC of 0.563 (95% CI = 0.543-0.582) when tested on an additional set of

SUBMITTER: Young LH 

PROVIDER: S-EPMC9339697 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature