## 4 Discussion and Conclusions
### Future Work  
Multiple future directions can be defined, given also the current limitations that are listed above. First, the size of the training set should be increased to exploit the capabilities of a deep neural network-based approach. To limit the complexity of the input data, we currently use the effective radius, however, we envision the use of the coronary lumen mask as input, which may then allow the model to consider lumen non-circularities for the prediction. The dimensionality of the input data would increase from 1D to 3D, which would require a larger training set for enabling an accurate prediction. Furthermore, with the increase in the dataset size, other deep-learning approaches (evaluated herein or others) might lead to the best FFR prediction performance. When employing a classification-based approach, another possible future direction is to increase the number of output classes. For example, a three-class approach would predict lesions as being functionally significant, functionally non-significant, or intermediate/uncertain. This would allow for the definition of hybrid decision-making strategy, where lesions which are not in the intermediate, i.e., uncertain class, can be confidently diagnosed, while for the ones in the intermediate class further aspects may be considered for the final decision, potentially even performing the invasive FFR measurement. The invasive FFR cut-off values for distinguishing the three classes may be chosen based on the performance of the model, e.g., to ensure a sensitivity/specificity of at least 95% for the lesions which are not in the intermediate class. The better the performance of the model, the closer the cut-off values may be to 0.8, i.e., the fewer lesions would be predicted as being uncertain. Herein, we have considered only the coronary lumen information as input. Previous studies have demonstrated that FFR is influenced also by other patient characteristics (demographics, other pathologies, etc.) [31]. The results of the sub-group analyses have shown the patient sex and age and the vessel of interest may influence the prediction. Additional features may be considered directly as input into the deep neural network, or a cascaded modeling approach may be designed: the first model processes only the coronary lumen information, while the second model, which takes as input the output of the first model, processes all additional features to perform a final and more accurate prediction. Standard OCT acquisitions have been used for obtaining the input data for the FFR prediction. OCT acquisition guidelines containing specific requirements (e.g., include the entire stenosis in the OCT sequence) may likely improve the prediction accuracy. Such an approach was successfully applied in a previous study [79]. The method that is described herein may be applied similarly on coronary lumen information that is extracted from other imaging modalities (XA, CCTA, IVUS). Since the image resolution, especially on XA and CCTA, is lower than on intra-vascular images, the coronary lumen information may be less accurate. However, XA and CCTA allow for a more complete evaluation of the coronary tree since the vessel of interest can be assessed in all its segments, alongside large side branches. A different methodology might lead to the optimal performance in that case, e.g., based on graph neural networks [80]. Finally, the approach can also be extended to predict other hemodynamic quantities, such as coronary flow reserve (CFR), rest Pd/Pa [81], the instantaneous wave-free ratio (iFR) [82], or hyperemic/basal stenosis resistance (HSR/BSR) [83; 84], each of which can be used as a ground-truth during training. Conceptualization, C.-A.H., I.-A.T. and L.M.I. ; methodology, C.-A.H., C.F.C., I.-A.T. and A.P. ; software, C.-A.H., A.P. and D.S. ; validation, L.C., N.-M.P.-F., V.B. and A.S.-U. ; formal analysis, C.F.C. ; investigation, L.C., N.-M.P.-F. and V.B. ; resources, I.-A.T. and L.M.I. ; data curation, I.-A.T. and L.C. ; writing--original draft preparation, C.-A.H., I.-A.T. and L.M.I.