# Quantitative Analysis of Retinal Vascular Leakage in Retinal Vascular 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 Behcet'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. u 202219  
and uses blue laser for FA. For detection of uveitis findings in the center to peripheral region, the current study used UWF FA images. FA is widely used for detecting inflammatory lesions in the posterior segment, such as vascular leakage and ocular complications, including the retinal non-perfusion area and neovascularization in uveitis. FA is useful for assessing disease activity and severity, as well as in monitoring the response to treatment [1; 2]. However, since the assessment of FA findings is subjective and descriptive, objective and quantitative measurements need to be developed for standardization of the clinical practice of uveitis [7]. Recently, machine learning has been applied for diagnosis and quantitative analysis of ocular findings in various retinal diseases, including diabetic retinopathy [8; 9]. Studies regarding the automated measurement of vascular leakage in the assessment of retinal vasculitis are limited [10; 11]. In a related recent study, leakages were automatically detected by evaluating the change over time in early- and late-phase UWF FA images [12]. Vascular segmentation was based on Gaussian operation, and registration was provided by using Fourier correlation of retinal vascular patterns. The intensity of leakage regions was equalized by spectral information, and leakage areas were binarized using a fixed threshold value. Parameters were set based on initial iterative feedback from expert readers on an initial preliminary training set. One problem to be solved, however, is the need for feedback from experts. The number of vessels in a UWF image is too great, and the vascular diameter is small; thus, vascular segmentation is challenging work for human experts. Conventional studies had to set many parameters for vascular segmentation, registration, and detection of the leakage area by expert feedback. Therefore, machine learning based on easy parameter setting should be discussed for leakage detection, although machine learning generally needs many images, and it is difficult to collect many uveitis UWF images. In the current study, we present an automated and quantitative analysis of retinal vascular leakage, including capillary leakage, using machine learning with a small dataset.