Quantitative Analysis of Retinal Vascular Leakage in Retinal Vasculitis Using Machine Learning

Abstract

Retinal vascular leakage is known to be an important biomarker to monitor the disease activity of uveitis. Although fluorescein angiography (FA) is a gold standard for the diagnosis and assessment of the disease activity of uveitis, the evaluation of FA findings, especially retinal vascular leakage, remains subjective and descriptive. In the current study, we developed an automatic segmentation model using a deep learning system, U-Net, and subtraction of the retinal vessel area between early-phase and late-phase FA images for the detection of the retinal vascular leakage area in ultrawide field (UWF) FA images in three patients with Behçet’s Disease and three patients with idiopathic uveitis with retinal vasculitis. This study demonstrated that the automated model for segmentation of the retinal vascular leakage area through the UWF FA images reached 0.434 (precision), 0.529 (recall), and 0.467 (Dice coefficient) without using UWF FA images for training. There was a significant positive correlation between the automated segmented area (pixels) of retinal vascular leakage and the FA vascular leakage score. The mean pixels of automatic segmented vascular leakage in UWF FA images with treatment was significantly reduced compared with before treatment. The automated segmentation of retinal vascular leakage in UWF FA images may be useful for objective and quantitative assessment of disease activity in posterior segment uveitis. Further studies at a larger scale are warranted to improve the performance of this automatic segmentation model to detect retinal vascular leakage.