## 4 Discussion and Conclusions  
### Deep Learning-Based Prediction of FFR  
As more data are emerging from studies that are based on artificial intelligence and computational modelling, the incremental diagnostic value of predicted coronary functional diagnostic indices over the traditional XA-based visual or quantitative lesion grading is becoming more evident. We have introduced a method for the deep-learning-based prediction of FFR from routine optical coherence tomography. No specific requirements were formulated for the OCT acquisition. We demonstrated that this approach has a high potential in assessing functionally significant stenoses. Different models and approaches were proposed and evaluated. The experiments indicated the superiority of the FSL-based approach, a type of DL formulation that is specialized for small datasets. However, given the large overlap in the 95% confidence intervals, the differences between the methods are statistically not significant. Thus, the main findings of this study can be summarized as follows: (1) DL-based FFR prediction from reduced-order raw anatomical data is feasible in a dataset that is focused on intermediate lesions for which the visual anatomical assessment of CAD based on XA does not allow for a clear clinical decision, and with no restriction on the type of lesions that were included in the study, and on the OCT acquisition; (2) DL-based FFR prediction provides superior diagnostic performance compared to baseline approaches based on MLD or %DS; (3) the FFR prediction performance increases quasi-linearly with the dataset size, indicating that a larger training dataset will likely lead to superior diagnostic performance. The diagnostic accuracy of 77.5% achieved herein is lower compared to that of other studies focusing on FFR prediction from OCT, which reported an accuracy ranging between 88% and 95% [21; 22; 31; 70; 71]. There are two main aspects that are responsible for this difference. First, the complexity of the dataset that is processed herein is higher than that of other studies: 46% of the samples have an invasive FFR value ranging between 0.75 and 0.85, while in other studies these grey zone lesions represented between 20% and 44% of the entire dataset [21; 22; 31; 70; 71]. Secondly, past studies focusing on FFR prediction from OCT either rely on computational fluid dynamics (CFD) [21; 22; 70; 71], or on ML-based approaches including hand-crafted features [31]. By applying a deep neural network directly on the raw data that are represented by the effective radius along the centerline of the vessel of interest, we allow the model to automatically learn powerful features for FFR prediction. The results that were obtained in other application areas (healthcare or others) demonstrate that classic machine learning (ML) techniques and hand-crafted features typically outperform DL-based approaches when the training set is small, but, conversely, the DL-based approaches outperform classic ML-based approaches when the size of the trainset increases significantly [70]. The results in Figure 4, depicting the accuracy as a function of the dataset size, confirm that a larger dataset will enable a better performance: the performance of the DL model increases quasi-linearly with the dataset size. As shown in Table 3, the diagnostic performance of the proposed model is already considerably higher outside of the 0.75-0.85 FFR value interval. To increase the prediction performance of DL models, different types of regularization are employed in the literature: mathematical expressions added to the loss function (L1, L2 regularization) [71], dropout (used to randomly drop out neurons during training) [72],and data augmentation [73]. Herein, we have used L2 regularizations and dropout. Data augmentation, i.e., generating new samples by perturbing the input data, is difficult to perform when training against invisively measured FFR, since the approximation of the ground truth values is not straightforward.