Abstract
Goal: To develop a cardiovascular virtual population using statistical modeling and computational biomechanics. Methods: A clinical data augmentation algorithm is implemented to efficiently generate virtual clinical data using a real clinical dataset. An atherosclerotic plaque growth model is employed to 3D reconstructed coronary arterial segments to generate virtual coronary arterial geometries (geometrical data). Last, the combination of the virtual clinical and geometrical data is achieved using a methodology that allows for the generation of a realistic virtual population which can be used in in silico clinical trials. Results: The results show good agreement between real and virtual clinical data presenting a mean gof 0.1 ± 0.08. 400 virtual coronary arteries were generated, while the final virtual population includes 10,000 patients. Conclusions: The virtual arterial geometries are efficiently matched to the generated clinical data, both increasing and complementing the variability of the virtual population.
Highlights
The design and development of new stents requires an assessment process to ensure their safety and efficacy
Impact Statement—We developed a unique virtual population of cardiovascular disease, which includes patients with clinical and arterial geometry data and it can be used for in-silico clinical trials
The lack of large number of patient populations and the invariability among the enrolled patients enhanced the need of creating virtual patients
Summary
The design and development of new stents requires an assessment process to ensure their safety and efficacy. This procedure consists of three phases of clinical trials on humans after the in vitro analysis and the in vivo assessment in animal studies. Each subsequent phase requires a different number of patients to be enrolled to secure the efficacy and safety of the new stent. Computational modelling and simulation enable in silico clinical trials towards reducing, refining, and partially replacing the real clinical trials [1] with significant benefits, in terms of cost, increased safety, and reduced side effects for the patients. In silico clinical trials are achieved through the utilization of computational models usually in the form of a medical digital twin and their application to human data. The lack of large number of patient populations and the invariability among the enrolled patients enhanced the need of creating virtual patients
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