## 2 Materials and Methods
### Subgroup Analyses  
In the following, we use the best performing model according to the results in Table 3 (FSL-CNN) to perform a series of subgroup analyses.  
As detailed in Section 2.1, the dataset contains a large number of samples in the interval 0.75-0.85 (46%). Hence, we have computed the statistics separately for lesions with FFR \(<\) 0.75, lesions with FFR \(>\) 0.85, and for the lesions with intermediate values. The results are displayed in Table 6. As expected, the accuracy of the model increases in the two bins at the extremes.  
In another analysis, we assessed the performance as a function of the vessel on which the measurement was performed. The results are displayed in Table 7 and indicate a higher accuracy on the LCx, compared to the other two main coronary arteries. The literature suggests that the LCx has typically a smaller baseline and hyperemic flow velocity compared to the LAD and RCA, which impacts the FFR measurements [67]. In other words, the same radius profile will lead to different invasive FFR values on different arteries. Since the type of artery is not used as an input to the DNN, a performance difference is expected.  
Most of the measurements in the study were performed in the LAD. The clinical literature suggests that proximal LAD lesions are of particular interest for long-term patient outcome [68]. Hence, we have divided LAD lesions into proximal lesions and others (mid or distal lesion). The results are displayed in Table 8 and indicate a similar performance in terms of accuracy, but the sensitivity is slightly lower for proximal lesions. This is an expected outcome since literature indicates that a lesion with a certain severity will lead to smaller FFR values when it is located in the proximal LAD, compared to the mid and distal LAD. Hence, the model slightly underestimates the severity of proximal LAD lesions.  
In another analysis, we assessed the prediction performance for male and female patients. The results in Table 9 indicate that the model performs slightly better for male patients. This is an expected outcome since the vast majority of lesions are from male patients (82%).  
\begin{table}
\begin{tabular}{c c c c} \hline
**FFR Interval** & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** \\ \hline FFR \(>\) 0.85 & 86.6 (70.3–94.6) & N/A & 86.6 (70.3–94.6) \\ \hline
0.75–0.85 & 68.0 (53.8–79.6) & 60.8 (40.7–77.8) & 75.0 (55.1–88.0) \\ \hline FFR \(<\) 0.75 & 84.0 (65.3–93.6) & 84.0 (65.3–93.6) & N/A \\ \hline \end{tabular}
\end{table}
Table 6: Diagnostic performance and 95% CI of the model for lesions with FFR \(<\) 0.75, lesions with FFR \(>\) 0.85, and for the lesions with intermediate values.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coronary Artery** & **Accuracy [\%]** & **Sensitivity [\%]** & **Specificity [\%]** \\ \hline LAD & 75.4 (62.8–84.7) & 76.4 (60.0–87.5) & 73.9 (53.5–87.4) \\ \hline LCX & 85.0 (58.3–91.9) & 80.0 (37.5–96.3) & 86.6 (54.8–92.9) \\ \hline RCA & 76.0 (56.5–88.5) & 55.5 (26.6–81.1) & 87.5 (63.9–96.5) \\ \hline \end{tabular}
\end{table}
Table 7: Diagnostic performance and 95% CI of the model for the three main coronary arteries.  
The age of the patient can be another important factor in the clinical decision-making. We have divided the data at the patient level into three equally large bins. The results in Table 10 indicate a marked difference between the three subgroups. The intermediate bin has a slightly larger number of intermediate lesions (18 vs. 15/14), partially explaining the difference in diagnostic performance.  
Finally, in another subgroup analysis we have considered the centerline length of the input data and have divided the samples into three equally sized bins. The results in Table 11 display a balanced performance, i.e., the considered length has no major influence on the model performance.