Cardiovascular patient-specific modeling: Where are we now and what does the future look like?

The clinical and bioengineering arenas are experiencing tremendous technological progress. Imaging technologies yield images with continuously improved timeand space-resolution; image processing is being boosted by the introduction of artificial intelligence (AI) techniques allowing for time-efficient, automated, and reliable segmentation of tissues or organs; and the performance of computational infrastructures is increasing exponentially. In this scenario, it is becoming easier and faster to exploit the anatomical and functional information generated by medical imaging to feed computer models meant to gain insights into clinically relevant scenarios. In their most basic version (Fig. 1), such models are detailed reconstructions that allow for the 3D quantification of the anatomy and of its motion, as well as for the quantification of clinically relevant functional metrics, e.g., 3D strain distributions as computed from ultrasound imaging (Rego et al., 2018) and fluid dynamic footprints of blood velocity fields as derived from 4D flow magnetic resonance imaging (Piatti et al., 2017). In clinics, these models provide physicians with exhaustive and non-misleading information to plan interventions: relevant examples are the choice of the best access and path to reach a target without harming noble structures, as in neurosurgery (Ferroli et al., 2013). The advent of augmented reality and virtual reality is empowering this type of model by providing the end-user with fully 3D and immersive renderings that allow for the better understanding of particularly complex anatomies, as in the case of patients whose anatomy is deranged by major congenital diseases (Butera et al., 2019). In their most sophisticated version (Fig. 1), patient-specific models simulate the physics of the analyzed organ or tissue (see, e.g., Krishnan et al., 2015; Collia et al., 2019). To this aim, the information on anatomy and motion of organs is complemented by the quantitative description of tissue micro-architecture (Lee et al., 2014)—e.g., myocardial fiber organization derived from diffusion tensor imaging (Whittaker et al., 2019)—and physical properties—e.g., mechanical properties, porosity, and electrical conductivity (Avazmohammadi et al., 2017). These models are expected to be high-fidelity replicas, or digital twins, of the tissues of organs affected by the disease. As such, they are used to simulate potential treatments and quantify the associated response of the relevant tissue or organ in terms of, e.g., tissue stresses (Rausch et al., 2017) or tissue remodeling (He et al., 2019; Boland et al., 2019). The computed variables are considered relevant, and sometimes pivotal, to predict potential acute intraor perioperative adverse events and longer-term effects of surgery. The potential of these models as tools to support decision making is in the possibility to define a set of parameters characterizing the envisioned treatment (e.g., type of technique; type, size, and location of the devices to be implanted and changes in patient conditions driven by drug therapy) and to systematically quantify the effects of the corresponding changes. Owing to their potential, these patient-specific image-based computational models are becoming ubiquitous in (i) industrial R&D, where they are used to virtually test the effects of prototypal devices and, consistently with the 3R principles, they are coupled with in vitro testing to reduce the need for animal models; (ii) regulatory processes, where not only the results of computational modeling of medical devices are considered crucial to their approval by agencies but also computational modeling can embody the actual medical device (Software as Medical Device—SAMD); and (iii) clinical practice, where computational models are used as SAMDs for the analysis of pathological conditions, prognosis, and decision making.