## 1 Introduction  
Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery disease (CAD) is the most prevalent CVD, accounting for 42% of these deaths. In CAD patients, plaque builds up in the coronary arteries and limits the blood flow to the myocardium, especially when the demand is increased (exercise, stress). In severe cases, this can lead to myocardial infarction, or even death. X-ray coronary angiography (XA) represents the gold standard in CAD imaging [1]. Optical coherence tomography (OCT) is used in certain scenarios in conjunction with XA. OCT has the highest resolution among all invasive imaging modalities, allowing for a precise intra-vascular evaluation of stent apposition and expansion [2; 3; 4], thus, representing a paramount tool for PCI (percutaneous coronary intervention) optimization [5]. Nonetheless, its ability to assess the functional significance of a stenosis is not negligible [6]. The purely anatomical assessment of CAD, independent from the medical imaging modality, does not fully capture the functional significance of coronary stenoses. In view of the limitations of the anatomical evaluation of CAD, Fractional Flow Reserve (FFR) has been introduced as a functional index. FFR is defined as the ratio of flow in the stenosed branch at hyperemia--a condition of stress, with maximum coronary blood flow--to the hypothetical hyperemic flow in the same branch under healthy conditions. This can be shown to be closely approximated by the ratio of hyperemic cycle-averaged pressure distal to the stenosis to the cycle-averaged aortic pressure [7]. An FFR value \(\leq\) 0.8 is considered to be positive, i.e., the patient requires invasive treatment, such as percutaneous coronary intervention (PCI-stenting) or coronary artery bypass graft (CABG). An FFR value > 0.8 is considered to be negative, i.e., typically only optimal medical therapy is prescribed. Several clinical trials have demonstrated the superiority of FFR-guided decision-making [8], which represents the current gold standard. However, although providing obvious advantages, studies indicate that the use of FFR is still relatively low due to the need to administer hyperemia-inducing drugs, additional costs, and the extended duration and invasive nature of the procedure [9]. Hence, computational approaches for FFR prediction have been introduced, relying either on computational fluid dynamics (CFD) or on artificial intelligence (AI). Blood-flow computations, performed using CFD, when used in conjunction with patient-specific anatomical models that are extracted from medical images, have been proposed for diagnosis, risk stratification, and surgical planning [4]. Model-based assessment of coronary stenoses has been previously performed using such techniques in several clinical studies, based on anatomical models that are reconstructed from coronary computed tomography angiography (CCTA) [10; 11; 12; 13], XA [14; 15; 16; 17; 18], or OCT [19; 20; 21; 22]. Computed FFR has been the main quantity of interest in these studies, all of which showed that computed FFR has good diagnostic accuracy compared to invasively measured FFR. The CFD models consist of partial differential equations, which can be only numerically solved, leading to a large number of algebraic equations. Due to the time-consuming process that is employed for reconstructing the anatomical model, and the computationally intensive aspect of the CFD models [23; 24], they are not used for intra-operative assessment and planning, where near real-time performance is required. Alternatively, artificial intelligence-based solutions may be employed that are capable of providing results in real-time. To develop such solutions, a large database is required for the training phase, containing pairs of input-output data. The input data are represented by the anatomical information, while the output are invasive FFR [25]. Once the training phase has been finalized, the online usage provides results instantaneously.