## 3 Results
### Classification Tasks
#### 3.2.1 Machine Learning  
Machine learning is an artificial intelligence technique that is based on the extraction of hand-crafted features which are then fed into a classifier, such as neural networks (NNs), support vector machines (SVM), or random forests (RF) [79]. In the context of retinal diseases, a recent review has been presented in literature that analyzes the quantitative parameters of retinal OCTA images that have been used in numerous studies [12]. Briefly, the main quantitative parameters that have been used are: blood vessel tortuosity (BVT), blood vessel caliber (BVC) or vessel diameter, blood vessel density (BVD or just VD), vessel perimeter index (VPI), foveal avascular zone area (FAZ-A), foveal avascular zone contour irregularity (FAZ-CI), vessel complexity index (VCI) such as the fractal dimension (FD), branchpoint analysis (BPA), differential artery-vein (A-V) analysis, flow analysis using parameters such as the flow index (FI) or flow void (FV), vessel branching coefficient, vessel branching angle, branching width ratio, and choroidal neurovascular (CNV) analysis. The mathematical description of these quantitative parameters is out of scope of this review, so interested readers can refer to the study by [PERSON] et al. [12] for a comprehensive analysis and definition of these parameters in quantitative OCTA image analysis. These quantitative parameters are based on the segmentation of the FAZ or of the blood vessels. When considering the vasculature parameters listed above, they are typically computed not on the output segmented image or volume but a thinning technique, often called skeletonization [80], is rather applied to  
Figure 5: Examples of analyzed classification methods. OCTA image available from the open ROSE dataset [13]. the vessel segmentation. This method reduces the vasculature to a centerline of the vessels and has been used in numerous other studies and imaging modalities [81; 82]. A few studies instead computed texture features, such as those based on a local binary pattern (LBP) analysis [83] or the wavelet transform [84], and either used only these features for classification or combined them with other standard quantification parameters that were previously listed. The most common machine learning method that was found for OCTA image classification was the support vector machine (SVM) [85]. This classifier was used for single disease detection, such as DR [70; 84] and glaucoma [24; 29], and was also employed for more complex classification tasks, such as DR staging [33] and distinguishing between different retinopathies [42]. The other classifiers that were used were NNs [32; 83; 86], k-means clustering [42], logistic regression [84], and a gradient boosting tree (XGBoost) [84]. Machine learning classification methods were used in basically all clinical applications, which included DR classification and staging, glaucoma classification, AMD classification, artery/vein classification, sickle cell retinopathy (SCR) classification and general retinopathy classification. When considering a general retinopathy classification, the study by [PERSON] et al. [42] used the features extracted from different areas (BVT, BVC, VPI, BVD, FAZ) and FAZ contour irregularity features within an SVM classifier and obtained a maximum accuracy of 97.45% when classifying between healthy and diseased images. When considering the different pathologies, the accuracy was slightly lower: 94.32% (DR vs. SCR). [PERSON] et al. [87] also presented a study for SCR classification, using the same features of [PERSON] et al. [42] and three different classifiers: SVM, KNN, and discriminant analysis. The best results were obtained using an SVM classifier, with a final accuracy equal to 97%. Again, [PERSON] at el. [30] presented a study also for artery/vein classification using a k-means clustering method, presenting an accuracy equal to 96.57% when considering all vessels. When considering AMD classification, [PERSON] et al.