# Quantitative Analysis of Retinal Vascular Leakage in Retinal Vascular Using Machine Learning
## 4 Discussion
The present study demonstrated that the automated model for segmentation of the retinal vascular leakage area through a subset of 12 UWF FA images reached 0.434 (precision), 0.529 (recall), and 0.467 (Dice coefficient), suggesting that the segmentation of retinal vascular leakage using UWF FA images is a challenging task for the current automatic segmentation model. Since brightness and contrast in FA images between the early-phase and late-phase images varied, normalization via pre-processing was performed in this study. In addition, since the signal intensity of the hyperfluorescent area in FA images was different between patients, we applied cluster analysis using the k-means method to extract the leakage area. Automated segmentation tended to over-extract the area between the leaking areas, as shown in Figure 3. Manual segmentation and automated segmentation were globally matched. However, this automated segmentation was based on unsupervised learning, so the Dice coefficient was not as high. As a result, the automated segmented area (pixel) and the FA score had a strong correlation, as shown in Figure 4. These results show the potential of automatic detection of the retinal vascular leakage area by registration of retinal vessels in early-phase and late-phase UWF FA images for the objective assessment of retinal vascular leakage in patients with uveitis. Retinal vascular leakage is known to be an important biomarker to monitor the disease activity of uveitis [26; 27]. For grading FA images, the FA scoring system proposed by the angiographic scoring for uveitis working group is widely used [7]. We investigated the correlation between the FA score and the retinal vascular leakage area (pixels) detected by our automatic segmentation system. We observed that there was a significant positive correlation between total FA score and pixels of the automated detected leakage area. Furthermore, the vascular leakage FA score was also significantly correlated with pixels of the automated detected leakage area. In addition, the mean pixels of automatic segmented vascular leakage, as well as the FA score, were significantly decreased compared with before treatment. Recently, [PERSON] et al. developed a U-Net based algorithm to detect retinal vascular leakage in the FA of patients with uveitis, and the trained algorithm obtained a Dice similarity coefficient of 0.563, suggesting that the algorithm can help clinicians more objectively compare vascular leakage between FA images [11]. U-Net is a supervised technique; thus, many annotated FA images are needed for U-Net training. Moreover, the results of leakage detection depend on annotation images made by human observers. It is difficult for a human observer to mark vascular leakage regions with high repeatability, because any abnormality is represented by a faint outline. Since the evaluation of FA images is subjective and descriptive, an objective and quantitative method to detect vascular leakage is needed to assess the disease activity in uveitis [7]. Our results suggest that automated quantification of the retinal vascular leakage area may contribute to the more precise and objective evaluation of inflammatory status in patients with uveitis in the posterior segment. In the present study, since the signal intensity was variable between FA images, normalization was performed on both early-phase images and late-phase images, and we determined the mean intensity and mean deviation for segmentation of vascular leakage as much as possible in each early/late UWF FA images. Since the mean intensity and mean deviation for normalization depends on the signal level of each UWF FA image, it was difficult to determine the fixed signal strength in the UWF FA images. Further investigations are needed for determination of appropriate normalization values for machine learning using UWF FA images. There were several limitations in this investigation. First, this study was a retrospective analysis and the sample size was small. Furthermore, the FA images were collected from a single institution.