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 50 images. The trained algorithm was then used to detect leakage on pairs of FA images from longitudinal patient visits. Longitudinal leakage follow-up showed a >2.21% change in the visible retina area covered by leakage (as detected by the algorithm) had a sensitivity and specificity of 90% (area under the curve [AUC] = 0.95) of detecting a clinically notable change compared to the gold standard, an expert clinician's assessment. Conclusions This deep learning algorithm showed modest concordance in identifying vascular leakage compared to ground truth but was able to aid in identifying vascular FA leakage changes over time. Translational Relevance This is a proof-of-concept study that vascular leakage can be detected in a more standardized way and that tools can be developed to help clinicians more objectively compare vascular leakage between FAs.

Assessment of a Novel Semi-Automated Algorithm for the Quantification of the Parafoveal Capillary Network

“… 37 , 38 Apart from segmentation of FA images for capillary network morphology, advances have also been performed in automated detection of vascular leakage. 39 …”

“…37,38 Apart from segmentation of FA images for capillary network morphology, advances have also been performed in automated detection of vascular leakage. 39 Several researchers have published works related to the development and improvement of algorithms for the quantitative assessment of the FAZ or the capillary bed surrounding FAZ analysing images obtained by invasive and noninvasive imaging modalities. The main limitation of these works relates to the lack of algorithm validation.…”