## 1 Introduction
Such supervised machine learning (ML) algorithms are routinely employed in medical imaging applications, e.g., organ segmentation [26]. Moreover, machine learning models can also be employed to reproduce the behavior of non-linear computational models [27; 28]. Recently, machine learning models for the prediction of FFR based on CCTA [29], XA [30], OCT [31], and intravascular ultrasound (IVUS) [32] have been introduced. All these approaches rely on the extraction of features describing the vascular geometry, specifically the arterial lumen, and, in some studies, also on patient features. The goal of the present study is to evaluate the feasibility of using deep neural networks (DNN) to predict the invasively measured FFR from the radius of the coronary lumen that is extracted along the centerline of the coronary artery of interest. The starting point is represented by OCT images, the coronary lumen is then automatically extracted for each cross-section and subsequently processed to determine an equivalent radius value. The radius values are then arranged in a one-dimensional (1D) sequence, to be fed as input to the DNN. Our approach is in contrast to previous ML-based approaches for FFR prediction,since we use as input raw, reduced-order anatomical data instead of hand-crafted features. The second important aspect of the study is that we focus on intermediate lesions, for which the visual anatomical assessment of CAD based on XA does not allow for a clear clinical decision. As a result, the dataset contains a large number of lesions having an FFR value that is close to the cut-off of 0.8, making the prediction task more challenging. Deep-learning (DL) is a class of machine learning algorithms that uses multiple layers to extract higher level features from the raw input [33]. The FFR prediction task can be formulated either as a regression problem (predict the exact value of FFR) or as a classification problem (predict the FFR class, e.g., binary classification: \(\leq\)0.8 or >0.8). There are several types of neural networks that are suitable for the FFR prediction, amongst others:  
* fully connected neural network, commonly referred to as artificial neural networks (ANNs). Potential disadvantages of ANNs are the large number of trainable parameters, which leads to the requirement of large training datasets, and the difficulty in capturing the inherent properties in 1D/2D/3D data structures
* convolutional neural networks (CNNs). Compared to ANNs, CNNs can capture the inherent properties in 1D/2D/3D data structures, but still require relatively large training sets. Also, fixed size input data are required if the network is not fully convolutional. * recurrent neural networks (RNNs) [34]. RNNs have the advantage that a variable length input sequence can be processed, but they may be affected by vanishing and exploding gradient issues. Few-shot learning (FSL) is a type of learning where the prediction is performed based on a limited number of samples [35]. In a study that was published by [PERSON] et al., the models that were used for FSL were classified into four categories: multitask learning, embedding learning, learning with external memory, and generative modeling. OCT images were previously used in a variety of DL-based applications: stent strut detection [36; 37], stent strut segmentation [38; 39; 40], coronary calcification segmentation [41; 42], atherosclerotic plaque characterization [43], and lumen segmentation [44]. Furthermore, DL-based approaches were employed also in studies addressing other types of optical signals [45; 46]. Herein, we evaluate the performance of ANNs, CNNs, and RNNs in both regression and classification formulations. Additionally, we also consider the use of FSL, focusing specifically on prototypical networks [47], a subcategory of the embedding learning models, considered the state of the art for classification tasks. More details that are related to prototypical networks are included in Appendix A.1.