Periodic Reporting for period 2 - PIC (Personalised In-Silico Cardiology)
Reporting period: 2019-09-01 to 2022-02-28
Cardiovascular diseases (CVD) have a huge impact on society in terms of mortality, morbidity and healthcare costs, being responsible for 1.9 million deaths in the EU annually (42% of all deaths) with a total cost of €169 billion. Improving healthcare systems in Europe in a period of ageing population and tightening financial constraints mandates a shift towards personalised and preventive management of disease. We need tailored and earlier treatments to increase the efficacy and efficiency of the healthcare system, as well as the quality of life of patients.
PIC is the European ITN that trained a cohort of 15 future innovation leaders able to articulate and materialise the vision of Personalised In-silico Cardiology where healthcare is guided by in-silico models. These models become virtual reconstructions of an individual, or avatars, to evaluate current health status and therapy options. PIC fellows built both mechanistic and statistical models from clinical data (WP1), enabling the extraction of biomarkers for better diagnosis and prognosis of the individual patient. PIC fellows then applied models to maximise the value of clinical data (WP2) to inform diagnosis, and to optimise clinical devices & drug choices (WP3) to deliver a personalised therapy.
The vision of a Personalised In-silico Cardiology materialised in the definition of 15 inter-related projects for each of the fellows (F1 to F15), setting the scope of the research into four main modelling aspects of the heart (anatomy, mechanics, electrophysiology, and fluid dynamics) and four cardiac conditions (heart failure, cardiomyopathies, arrhythmias and flow obstructions). The specific objectives for the project are:
- WP1 focuses on the in-silico modelling technology. Its objectives are to develop the simulation methodologies, and to obtain robust biomarkers by cardiac model personalization. Interpreting clinical data through biophysical models allows the extraction of the underlying physiological parameters that best explain the data.
- WP2 focuses on the data. Its objectives are to use insilico cardiac models to reduce errors in clinical data, reduce invasiveness, and to maximise its diagnostic and prognostic value.
- WP3 focuses on the technologies for therapy: clinical devices and cardiac drugs. Its objective is to personalize these technologies through the adoption of the in-silico methodology and its predictions.
- WP4 focuses on the clinical translation of the in-silico technology. Its objective is to evaluate in specific CVD problems the envisioned improved care through better data, diagnosis and therapies.
PIC is the European ITN that trained a cohort of 15 future innovation leaders able to articulate and materialise the vision of Personalised In-silico Cardiology where healthcare is guided by in-silico models. These models become virtual reconstructions of an individual, or avatars, to evaluate current health status and therapy options. PIC fellows built both mechanistic and statistical models from clinical data (WP1), enabling the extraction of biomarkers for better diagnosis and prognosis of the individual patient. PIC fellows then applied models to maximise the value of clinical data (WP2) to inform diagnosis, and to optimise clinical devices & drug choices (WP3) to deliver a personalised therapy.
The vision of a Personalised In-silico Cardiology materialised in the definition of 15 inter-related projects for each of the fellows (F1 to F15), setting the scope of the research into four main modelling aspects of the heart (anatomy, mechanics, electrophysiology, and fluid dynamics) and four cardiac conditions (heart failure, cardiomyopathies, arrhythmias and flow obstructions). The specific objectives for the project are:
- WP1 focuses on the in-silico modelling technology. Its objectives are to develop the simulation methodologies, and to obtain robust biomarkers by cardiac model personalization. Interpreting clinical data through biophysical models allows the extraction of the underlying physiological parameters that best explain the data.
- WP2 focuses on the data. Its objectives are to use insilico cardiac models to reduce errors in clinical data, reduce invasiveness, and to maximise its diagnostic and prognostic value.
- WP3 focuses on the technologies for therapy: clinical devices and cardiac drugs. Its objective is to personalize these technologies through the adoption of the in-silico methodology and its predictions.
- WP4 focuses on the clinical translation of the in-silico technology. Its objective is to evaluate in specific CVD problems the envisioned improved care through better data, diagnosis and therapies.
PIC was the European ITN that trained the cohort of 15 innovation leaders able to articulate and materialise the vision of a Personalised In-silico Cardiology (PIC). It addressed the specific challenges originated by cultural and structural barriers between sectors and disciplines, articulating a fluent dialogue and work between clinicians and engineers. PIC has defined a new area of research and set the vision of how the digital twin is the pathway towards precision medicine in cardiovascular medicine.
The barriers between sectors were opened through the personal mutual trust built both among the PIC fellows and the supervisors. Sharing the vision of the Personalised In-silico Cardiology was the main enabler to bring the sectors and disciplines together. The main success indicator of the quality of the training was the external recognition received by fellows in scientific meetings, with a solid track of publications and awards. Publications also reflect the tight collaboration among academic, clinical, industrial, and regulatory partners – our white paper titled “The digital twin to enable the vision of precision cardiology” [1], integrating all beneficiaries of the consortium in the major journal in the field of cardiology, was the main exponent of this collaboration. There are several pieces of evidence of the lasting collaboration among the beneficiaries. The pillars of mutual trust, the complementarity between beneficiaries, and the collaborative spirit built by the network, led to the “Cardiovascular Digital Twin” Doctoral Training proposal, submitted last November 2021.
The main outreach events were our three international Summer Schools, one in Oslo, one in Barcelona and last one online due to Covid restrictions, hitting attendance numbers much larger than we ever expected (around 50 and 150 in the physical and online versions respectively), and thus creating a unique opportunity for our fellows and other researchers to network with word leaders in the area of computational cardiology. These events were very useful to articulate synergies with “sister Marie Skłodowska-Curie Actions”, such as AFibNet (g.a. 675351), CardioFunXion (g.a. 642676) or PersonaliseAF (g.a. 860974).
With respect to exploitable results the work of PIC fellows hosted in industrial beneficiaries has directly contributed to their products and services (enhanced prediction of risks in the planning of valve surgeries in FEOPS, a new concept for pacemaker sensing and monitoring in Medtronic, and the automation of workflow and analysis in the quantification of echocardiographic scans in GE). PIC fellows hosted in academic beneficiaries have generated ideas and tools with exploitation potential, such as the prediction of risks after an infarct based on the spatio-temporal 3D analysis of the heartbeat captured in an MRI, or the use of mobile phones as a digital stethoscope to screen and detect the presence of cardiac disease.
The barriers between sectors were opened through the personal mutual trust built both among the PIC fellows and the supervisors. Sharing the vision of the Personalised In-silico Cardiology was the main enabler to bring the sectors and disciplines together. The main success indicator of the quality of the training was the external recognition received by fellows in scientific meetings, with a solid track of publications and awards. Publications also reflect the tight collaboration among academic, clinical, industrial, and regulatory partners – our white paper titled “The digital twin to enable the vision of precision cardiology” [1], integrating all beneficiaries of the consortium in the major journal in the field of cardiology, was the main exponent of this collaboration. There are several pieces of evidence of the lasting collaboration among the beneficiaries. The pillars of mutual trust, the complementarity between beneficiaries, and the collaborative spirit built by the network, led to the “Cardiovascular Digital Twin” Doctoral Training proposal, submitted last November 2021.
The main outreach events were our three international Summer Schools, one in Oslo, one in Barcelona and last one online due to Covid restrictions, hitting attendance numbers much larger than we ever expected (around 50 and 150 in the physical and online versions respectively), and thus creating a unique opportunity for our fellows and other researchers to network with word leaders in the area of computational cardiology. These events were very useful to articulate synergies with “sister Marie Skłodowska-Curie Actions”, such as AFibNet (g.a. 675351), CardioFunXion (g.a. 642676) or PersonaliseAF (g.a. 860974).
With respect to exploitable results the work of PIC fellows hosted in industrial beneficiaries has directly contributed to their products and services (enhanced prediction of risks in the planning of valve surgeries in FEOPS, a new concept for pacemaker sensing and monitoring in Medtronic, and the automation of workflow and analysis in the quantification of echocardiographic scans in GE). PIC fellows hosted in academic beneficiaries have generated ideas and tools with exploitation potential, such as the prediction of risks after an infarct based on the spatio-temporal 3D analysis of the heartbeat captured in an MRI, or the use of mobile phones as a digital stethoscope to screen and detect the presence of cardiac disease.
PIC delivered solid steps towards a better healthcare based on computational models of the heart, exploiting the in-silico reasoning power of machines based on both inductive (i.e. based on statistical modles) and deductive (i.e. based on mechanistic models) logic. The 15 PIC fellows worked towards this overarching goal, addressing different specific problems as part of their research projects.
PIC has strengthened the competitiveness and growth of our industrial beneficiaries. FEOPS, an SME, has improved its technological ability to predict risks in the surgical implant of valves; Medtronic has a new prototype sensor, and the evidence of the ability to infer cardiac mechanical physiological parameters (such as pressure) that are key for the control of pacemakers; and GE has improved the efficiency of its analysis technology, automating previously manual steps and generating more robust and reproducible contouring solutions.
PIC has enhanced the innovation capacity of their beneficiaries. PIC has proposed to the scientific community the concept of the combination of the mechanistic and statistical models for the construction of digital twins of the cardiovascular system. This new innovative concept brings together several disciplines and sectors, and is delivering already its early success stories, such as the improved ability to predict risks in the simulation of valve implants provided by our SME, FEOPS. PIC has also delivered a specific product, Echoes, a mobile app that converts a conventional mobile phone in a digital stethoscope able to capture the heart sounds.
PIC has strengthened the competitiveness and growth of our industrial beneficiaries. FEOPS, an SME, has improved its technological ability to predict risks in the surgical implant of valves; Medtronic has a new prototype sensor, and the evidence of the ability to infer cardiac mechanical physiological parameters (such as pressure) that are key for the control of pacemakers; and GE has improved the efficiency of its analysis technology, automating previously manual steps and generating more robust and reproducible contouring solutions.
PIC has enhanced the innovation capacity of their beneficiaries. PIC has proposed to the scientific community the concept of the combination of the mechanistic and statistical models for the construction of digital twins of the cardiovascular system. This new innovative concept brings together several disciplines and sectors, and is delivering already its early success stories, such as the improved ability to predict risks in the simulation of valve implants provided by our SME, FEOPS. PIC has also delivered a specific product, Echoes, a mobile app that converts a conventional mobile phone in a digital stethoscope able to capture the heart sounds.