# Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments
## 4 Discussion  
The availability of in silico models of patient cohorts is gaining importance due to the growing interest in the development of in silico trials for the assessment of the safety and efficacy of new medical devices and procedures. When the in silico trial is based on high-fidelity simulations of a clinical procedure, a large amount of high-quality Computerized Tomography or Magnetic Resonance Imaging data are required for the generation of patient-specific 3D anatomical domains, together with other clinical data needed as input for the procedure modeling. However, high-quality imaging is not always available, or accessible, for instance, due to patient privacy policies, and gathering a large number of such high-quality images may not be feasible in clinical practice. In this regard, the implementation of methodologies for the generation of synthetic data, starting from a limited real patients' dataset, can offer a solution for the creation of large virtual populations suitable for in silico trials. Statistical shape modeling is a well-established methodology [23; 29; 30; 31] which, based on dimensionality reduction techniques and parameter sampling, allows one to identify the principal modes of shape variation of an object and to generate new samples. The SSM technique has been used in the literature for the generation of virtual cohorts of cardiac [17] and aortic [19; 20] models. This work proposed the use of the SSM approach for the generation of virtual populations of cerebrovascular anatomies, useful for the development of in silico trials for AIS treatments. In [8], the first proof-of-concept in silico stroke trial was implemented, based on a surrogate model of the thrombectomy procedure trained on a dataset of 94 high-fidelity thrombectomy simulations run in patient-specific vascular models. The methodology described in the present work would enable the creation of the desired number of virtual stroke patients for increasing the reliability of an in silico stroke trial. Moreover, this methodology also allows one to generate virtual patients with a variety of anatomic characteristics, which is important to assess the efficacy of a stroke treatment. In fact, several studies in the literature, based on the analysis of clinical data or on computational approaches, demonstrated the impact of geometric parameters, such as arterial diameters, curvatures and ICA tortuosity, on the outcome of thrombectomy procedures [9; 10; 11; 12; 13]. Figure 7: (**A**) Segmented STL geometry of the phantom lumen (gray) superimposed on the reconstructed CAD geometry (red); (**B**) contour map of the absolute distance between STL and CAD geometry normalized with the local diameters plotted on the CAD geometry. As a proof-of-concept of the methodology, a virtual population of 100 cerebrovascular anatomies was generated using an SSM methodology with random sampling of the principal shape modes following the normal distributions fitted to the principal values of the training dataset. The methodology allowed the generation of the centerlines of the main cerebral vessels (ICA, ACA, M1, and M2) and the associated diameters. Subsequently, an acceptance criterion was established, based on 19 geometric parameters. Of the 100 synthetic anatomies, 17 had at least one of the parameters outside the range allowed by the analysis of the data used for the training of SSM and were excluded from the final population. The comparison of the geometric parameters' distribution (Figure 6 and Appendix A) demonstrated that the generated virtual population represents the data variability in the original training dataset well. The Mann-Whitney U-test assessed that, for 13 out of the 19 considered geometric parameters, the parameter distribution in the generated population had a median equal to the one in the training dataset, indicating that the generated population represents the characteristics observed in the training dataset well. Different sampling strategies may lead to different results on the distribution of the geometric parameters.