Spanjaards, M., Borowski, F., Supp, L., Ubachs, R., Lavezzo, V., and van der Sluis, O., 2024. A fast in silico model for preoperative risk assessment of paravalvular leakage. Biomechanics and Modeling in Mechanobiology.

Eisenberg, S., Reiss, M., Czypionka, T., Kraus, M. and Rösler, D., 2023. A conceptual framework to evaluate the socioeconomic impact of in-silico models for implantable medical devices. Population Medicine, 5(Supplement).

Eisenberg, S., Czypionka, T., Kraus, M., Reiss, M. and Rösler, D., 2023. The socioeconomic impact of in-silico models for implantable medical devices: a conceptual framework. European Journal of Public Health, 33(Supplement_2), pp.ckad160-576.

Oldenburg, J., Borowski, F., Wollenberg, W., Öner, A., Schmitz, K.P., and Stiehm, M., 2023. Augmentation of experimentally obtained flow fields by means of Physics Informed Neural Networks (PINN) demonstrated on aneurysm flow. Current Directions in Biomedical Engineering, vol. 9, no. 1, 2023, pp. 519-523.

Matei, T. I., Popescu, A. B., Nita, C. I., Ciusdel, C. F., & Itu, L. M., 2023. CFD-based synthetic data generation for machine learning based pressure drop assessment in aortic stenosis. Studies in Informatics and Control, 32(4), 49-58.

Willems, R., Verberne, L., van der Sluis, O.,Verhoosel C.V., 2023. Echocardiogram-based ventricular isogeometric cardiac analysis using multi-patch fitted NURBS. Computer Methods in Applied Mechanics and Engineering.

Hilhorst, P. L., Quicken, S., van de Vosse, F. N., & Huberts, W., 2023. Efficient sensitivity analysis for biomechanical models with correlated inputs. International journal for numerical methods in biomedical engineering.

Goubergrits L., Schafstedde M., Cesarovic N., Szengel A., Schmitt B., Wiegand M., Romberg J., Arndt A., Kuehne T., Brüning J., 2023. CT-based comparison of porcine, ovine, and human pulmonary arterial morphometry. Springer Nature

Verstraeten S., Hoeijmakers M., Tonino P., Brüning J., Capelli C., van de Vosse F., Huberts W., 2023. Generation of synthetic aortic valve stenosis geometries for in silico trials. International Journal of Numerical Methods in Biomedical Engineering.

Aydin B., Kiely E., Ohmann C., 2023. Feasibility assessment of using CDISC data standards for in silico medical device trials. Journal of the Society of Clinical Data Management.

Brüning J., Yevtushenko P., Schlief A., Jochum T., Van Gijzen L., Meine S., Romberg J., Kuehne T., Arndt A., Goubergrits L., 2023. In-silico enhanced animal study of pulmonary artery pressure sensors: assessing hemodynamics using computational fluid dynamics. Frontiers in Medical Technology.

Stoter, S.K.F., Divi, S.C., M., Harald van Brummelen, E., Larson, M.G., De Prenter, F.,  Verhoosel, C.V., 2023. Critical time-step size analysis and mass scaling by ghost-penalty for immersogeometric explicit dynamics. Computer Methods in Applied Mechanics and Engineering.

Lesage, R., Van Oudheusden, M., Schievano, S., Van Hoyweghen, I., Geris, L., Capelli, C., 2023. Mapping the use of computational modelling and simulation in clinics: A survey. Frontiers in Medical Technology.

Oldenburg, J., Borowski, F., Kaule, S., Schmitz, KP., Öner, A. & Stiehm, M., 2023. Analysis on the effects of hypo-attenuated leaflet thickening on the hemodynamics of transcatheter aortic valve prostheses by means of particle image velocimetry. tm – Technisches Messen.

Borowski, F., Kaule, S., Oldenburg, J., Schmitz, K.P., Öner, A., Stiehm, M., 2023. Analysis of thrombosis risk of commissural misaligned transcatheter aortic valve prostheses using particle image velocimetry. tm -Technisches Messen.

Borowski, F., Ott, R.,  Oldenburg, J., Kaule, S., Öner, A., Schmitz, K.P., Stiehm, M., 2022. Validation of a Fluid Structure Interaction Model for TAVR using Particle Image Velocimetry. Current Directions in Biomedical Engineering, 8(2), 512-515.

Oldenburg, J., Borowski, F., Schmitz, K. & Stiehm, M., 2022. Computation of flow through TAVI device by means of physics informed neural networks. Current Directions in Biomedical Engineering, 8(2), 741-744.

Oldenburg, J., Borowski, F., Öner, A., Schmitz, K.P. and Stiehm, M., 2022. Geometry aware physics informed neural network surrogate for solving Navier-Stokes equation (GAPINN). Advanced Modeling and Simulation in Engineering Sciences 9, 8.

Krüger, N., Meyer, A., Tautz, L., Hüllebrand, M., Wamala, I., Pullig, M., Kofler, M., Kempfert, J., Sündermann, S., Falk, V. and Hennemuth, A., 2022. Cascaded neural network-based CT image processing for aortic root analysis. International Journal of Computer Assisted Radiology and Surgery, 17(3), pp.507-519.

Musuamba, F.T., Skottheim Rusten, I., Lesage, R., Russo, G., Bursi, R., Emili, L., Wangorsch, G., Manolis, E., Karlsson, K.E., Kulesza, A. and Courcelles, E., 2021. Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility. CPT: Pharmacometrics & Systems Pharmacology, 10(8), pp.804-825.

Contin, M., Davide Montesarchio, D., Van Horenbeeck, Z., Lesage, R., Platonov, A., Stanic, G., De Michele, R., Rangarajan, J. R., & Geris, L. (2024). An in silico medicine info kit for effective stakeholder engagement. Virtual Physiological Human Conference 2024 (VPH 2024), Stuttgart, 4-6 September 2024.

Van Horenbeeck, Z., Lesage, R., Platonov, A., Contin, M., van Oudheusden, M., Lievevrouw, E., Staumont, B., Rangarajan, J. R., Schievano, S., Van Hoyweghen, I., Capelli, C., & Geris, L. (2024). Computer modelling and simulation in clinics: Longitudinal mapping of usage and clinician’s trust in in silico medicine. Virtual Physiological Human Conference 2024 (VPH 2024), Stuttgart, 4-6 September 2024.

Goubergrits, L., Yevtushenko, P., Schlief, A., Romberg, J., Kuehne, T., Arndt, A., & Bruening, J. (2024). Hemodynamics of an implanted pressure sensor in porcine and human pulmonary artery. Virtual Physiological Human Conference 2024 (VPH 2024), Stuttgart, 4-6 September 2024.

Brüning, J., Schlief, A., Yevtushenko, P., Romberg, J., Arndt, A., & Goubergrits, L. (2024, settembre 13). Inform design of a pulmonary artery pressure sensor using virtual cohorts. Virtual Physiological Human Conference 2024 (VPH 2024), Stuttgart, 4-6 September 2024.

Brüning, J., Schlief, A., Yevtushenko, P., Romberg, J., Arndt, A., & Goubergrits, L. (2024). In-silico enhanced animal experiments for evaluation of cardiovascular implantable devices. Virtual Physiological Human Conference 2024 (VPH 2024), 2-6 September 2024, Stuttgart.

Sivera, R., Montgomery-Liljeroth, E., Cook, A., Schievano, S., Patel, K., & Capelli, C. (2024). Unveiling the relation between aortic shape and calcification in population with aortic stenosis: Towards better management of TAVI patients. Virtual Physiological Human Conference 2024 (VPH 2024), Stuttgart, 4-6 September 2024.

Verstraeten, S., Thissen, D., Hoeijmakers, M., van de Vosse, F., & Huberts, W. (2024). Virtual cohort generation for in silico trials of transcatheter aortic valve implantation. Virtual Physiological Human Conference 2024 (VPH 2024), Stuttgart, 4-6 September 2024.

Verstraeten, S., Hoeijmakers, M., van de Vosse, F., Huberts, W., 2024. A fluid-structure interaction approach to distinguish between true and pseudo-severe aortic stenosis. 19^th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE 2024), 30 July – 1 August 2024.

Brüning, J., Schlief, A., Yevtushenko, P., Romberg, J., Arndt, A., Goubergrits, L., 2024. In-silico enhanced chronic animal experiment to investigate cardiovascular implantable devices. 29th Congress of the European Society of Biomechanics (ESB 2024), 30 June – 3 July 2024.

Verstraeten, S., Hoeijmakers, M., van de Vosse, F., Huberts, W., 2024. A fluid-structure interaction simulation framework to distinguish between true and pseudo-severe aortic stenosis. 29th Congress of the European Society of Biomechanics (ESB 2024), 30 June – 3 July 2024.

Schlief, A., Goubergrits, L., Brüning, J., 2024. A fast-to-evaluate model to inform device design decisions for pulmonary artery pressure sensors. 29th Congress of the European Society of Biomechanics (ESB 2024), 30 June – 3 July 2024.

Roesler, D., Czypionka, T., Eisenberg, S., Kraus, M., Reiss, M., Roesler, D., 2024. The socioeconomic impact of in-silico methods for implantable medical devices: a conceptual framework, European Health Management Association (EHMA) 2024 Abstract Book, 108.

Czypionka, T., Eisenberg, S., Kraus, M., Reiss, M., Rösler, D., 2023. A conceptual framework to evaluate the socioeconomic impact of in-silico models for implantable medical devices, European Health Management Association (EHMA) 2023 Abstract Book, 328-329.

Czypionka, T., Eisenberg, S., Kraus, M., Reiss, M., Rösler, D., 2023. A conceptual framework to evaluate the socioeconomic impact of in-silico models for implantable medical devices. Austrian Health Economics Association (ATHEA) 2023 Book of Abstracts, 29-30.

Hatfaludi, C.A., Bunescu, D., Ciusdel, C.F., Serban, A., Bose, K., Oppel, M., Schroder, S., Seehase, C., Langer, H. F., Erdmann, J., Nording, H., Itu, L. M., 2023. Deep learning based detection of collateral circulation in coronary angiographies. 23 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS), 22-24 June 2023.

Verstraeten, S., Hoeijmakers, M., van de Vosse, F., Huberts, W. Virtual cohort generation for in silico trials of transcatheter aortic valve implantation. 28th Annual Conference of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Brüning, J., Krüger, N., Yevtushenko, P., Goubergrits, L., Inter-species differences in pulmonary artery morphometry and hemodynamics. 28th Annual Conference of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Spanjaards, M., Ubachs, R., Lavezzo, V., Van der Sluis, O. Pre-operative risk assessment of paravalvular leakage using a computational tavi deployment model. 28th Annual Conference of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Moradi, H., van de Vosse, F., Huberts, W. Potential of using shell elements methods in fsi simulations of pulmonary arteries. 28th Annual Conference of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Oldenburg, J., Borowski, F., Kaule, S., Öner, A., Schmitz, K.P., & Stiehm, M., 2022. Measurement of steady flow through a transcatheter aortic valve replacement by means of particle image velocimetry. German Association for Laser Anemometry (GALA) Annual Meeting.

Hilhorst, P., Quicken, S., Van de Vosse, F., and Huberts, W. 2022. Sensitivity analysis of an one-dimensional pulse wave propagation model with correlated input. Virtual Physiological Human 2022 Conference.

Lesage, R., Van Oudheusden, M., Contin, M., Schievano, S., and Capelli, C. 2022. Computer modelling and simulation in clinics: mapping usage and opinions for advancing in silico medicine. Virtual Physiological Human 2022 Conference.

Verstraeten, S., Suasso de Lima de Prado, D., Hoeijmakers, M., Van de Vosse, F., and Huberts, W. 2022. Non-parametric statistical shape modelling for in silico trials of TAVI. Virtual Physiological Human 2022 Conference.

Brüning, J., Yevtushenko, P., and Goubergrits, L. 2022. Validation of a synthetic cohort of aortic stenosis patients. Virtual Physiological Human 2022 Conference.

Moradi, H., Van de Vosse, F., and Huberts, W. 2022. Identification of the most influential factors on pulmonary artery hemodynamics using variance-based sensitivity analysis. Virtual Physiological Human 2022 Conference.

Verhoosel, C. V., Harald van Brummelen, E., Divi, S. C., & de Prenter, F. (2023). Scan-Based Immersed Isogeometric Flow Analysis. Frontiers in Computational Fluid-Structure Interaction and Flow Simulation: Research from Lead Investigators Under Forty-2023, 477-512.

Ardnt, A. From engineering metrics to clinical endpoints. ESB2023 pre-course series, pre-course IV. From patient-based to in-silico trials: status quo and future perspectives.

Verde, P. Patient-based clinical trials. ESB2023 pre-course series, pre-course IV. From patient-based to in-silico trials: status quo and future perspectives.

Brüning, J. A statistical shape model of the porcine and human pulmonary artery for evaluation of medical devices. ESB2023 pre-course series, pre-course III. Virtual cohort generation, validation and application.

Verstraeten, S. Virtual cohort generation of aortic valve stenosis geometries. ESB2023 pre-course series, pre-course III. Virtual cohort generation, validation and application.

Huberts, W. Virtual cohort generation and validation: a multi-level methodology. ESB2023 pre-course series, pre-course III. Virtual cohort generation, validation and application.

D9.6 – Devices approval experience report (BIO, M39)

This report provides an overview of the regulatory dossier (Pre-Submission) that BIOTRONIK submitted to the United States Food and Drug Administration (FDA) to gather feedback on the design of in-silico-clinical-trials for the US regulatory approval of the proprietary PAPS device.

D2.6 – Regulatory feedback report (2) (VPH, M40)

This deliverable reports the interactions of the consortium with regulatory stakeholders in M20-M39 of the project.

D2.4 – Dissemination events (LYN, M42)

Report of the dissemination events (conferences, workshops, symposia, focus groups) attended by consortium partners or (co)organised by the consortium throughout the project and beyond.

D2.3 – Communication channels and materials (LYN, M42)

Report of the project communication channel activity and the production of communication and dissemination materials.

D7.8 – Validated virtual cohorts for in-silico trials (TUE, M36)

D4.6 – SOPs for validation of in silico models (IIB, M36)

D4.3 – SOPs for virtual cohorts generation and validation (TUE, M36)

Standard operating procedure (SOP) on the generation of virtual cohorts and their validation for use in in-silico trials, as a systematic approach to building and validating virtual cohorts, for immediate use in device-effect simulation and in-silico trials.

D8.7 – Reduced-order model (PHI, M30)

Description of the workflow for creating a Reduced-Order Model (ROM) to generate input for the fast-to-evaluate deployment model developed in D8.8 – Fast-to-evaluate TAVI model based on multi-patch NURBS (TUE).

D6.5 – Specification and quantification of synthetic boundary conditions (CHA, M30)

Description of the specifications for the generation of synthetic boundary conditions, that are required to describe virtual patients or datasets to be analysed via in-silico modelling, or to replace or impute missing information for in-silico assessment of real animal- or patient-specific cases.

D8.8 – Fast-to-evaluate TAVI model based on multi-patch NURBS (TUE, M28)

Description of the fast-to-evaluate TAVI deployment model that provides quantitative insights into the positioning of the implanted device and its effect on post-implantation flow characteristics.

D9.1 – Constitutive vessel model (TUG, M27)

Report on the development of an enhanced constitutive model to describe the anisotropic material behaviour of the vessel wall, which will be used in combination with the virtual device models for preliminary analyses requiring a reduced computational effort.

D8.4 – Validated constitutive models of the vessel wall (TUG, M26)

Report on the mechanical experiments and multi-photon microscopy imaging methods used to obtain the mechanical parameters and microstructural information of the ascending aorta and the pulmonary artery (main, left and right pulmonary artery) from healthy sheep, porcine and human tissue.

D8.6 – Report on 3D finite element simulations (PHI, M24)

Report on the numerical models developed to perform device implantation and device effect simulations for TAVI.

D8.5 – Fast device deployment model (CHA, M24)

Report on the development of efficient physics-based models for simulation of device deployment and the implementation of a prototype for the use cases of PAPS and TAVI.

D6.4 – Specification and quantification of subject-specific data-based boundary conditions (CHA, M24)

Description of the assessment of boundary conditions required for in-silico assessment of device efficacy and safety for both project use cases, transcatheter aortic valve implantation (TAVI) and pulmonary artery pressure sensor (PAPS), using patient-specific information.

D4.5 – SOPs for in-silico analysis of TAVI (IIB, M24)

Standard operating procedures (SOPs) on flow field assessment of transcatheter aortic valve implants (TAVI) by means of numerical simulation applying fluid structure interaction (FSI) and particle image velocimetry measurements.

D6.3 – Uncertainty quantification for input data (CHA, M21)

Report of the uncertainty analysis of geometrical and functional model input data obtained by processing CT, MRI and sensor data.

D8.3 – Constitutive vessel model (TUG, M20)

Report on all relevant information to develop a simplified constitutive model to describe the material behaviour of the vessel wall, to be used in combination with the virtual device models for preliminary analyses requiring less computational effort.

D2.5 – Regulatory feedback report (1) (VPH, M19)

D7.5 – Uncertainty quantification and re-definition of input space (TUE, M18)

Demonstration of how the sensitivity analyses can be used to redefine the input space.

D4.1 – SOPs for data acquisition for in-silico models (UCL, M18)

Standard operating procedure (SOP) on clinical (secondary use) and preclinical in-vivo data acquisition in order to create virtual cohorts for in-silico modeling.

D7.4 – Sensitivity and uncertainty quantification toolbox (TUE, M16)

Proof-of-principle of the application of uncertainty quantification and sensitivity analysis to both aortic valve stenosis and heart failure patients.

D10.1 – In-silico trial impact assessment framework (ECRIN, M12)

Description of the conceptual framework for the in-silico trial impact assessment.

D7.3 – First version of the definition of the input space (TUE, M12)

First version of the definition of the input space that will be used to feed the virtual cohort generators.

D4.4 – Guidelines for documentation (IIB, M12)

Recommendations on the formatting, organization, and content of reports describing in-silico modelling and results in the field of computational fluid dynamics (CFD) and finite element analysis (FEA) for medical device regulatory submissions.

D4.2 – Standard operating procedure for data processing for in-silico models (CHA, M12)

Standard operating procedure (SOP) on clinical data processing for creating virtual cohorts for in-silico models.

D7.2 – Definition of model templates (TUE, M9)

Preliminary definition of model templates that will be used during virtual cohort generation of both aortic valve disease and heart failure patients.

D1.5 – Quality assurance guidelines (LYN, M8)

Set of guidelines adopted for ensuring the highest quality in the execution of the project, including project management procedures, procedures for the preparation and quality control of project deliverables, reports and software.

D7.1 – Definition of model output (TUE, M6)

Preliminary definition of the physiological outputs to be used during virtual patient cohort generation of heart failure and aortic valve disease patients.

D3.2 – Data management plan (LYN, M6)

Comprehensive analysis of the nature of data to be handled, generation, collection, de-identification and other processing, data flow and usage in the context of the project research activity and beyond, accessibility, interoperability, FAIR, long-term storage and backup, security measures adopted to prevent unauthorised access to personal data in the virtual research environment, and procedures for the inclusion of data and other resources in the European Open Science Cloud.

D3.1 – System requirements (UTBV, M6)

D1.4 – Self-assessment plan (LYN, M6)

Self-assessment plan of the project, including key performance indicators and procedures for completing the self-assessment for each work package.

D6.1 – Specification of data processing requirements (CHA, M4)

Requirements for medical image data processing for TAVI and PAPS use cases, including information to be extracted from medical images, relevant parameters and quality criteria.

D1.3 – Project handbook (LYN, M4)

Reference document for day-to-day project management, recapitulating project legal and ethical aspects, consortium partner composition and roles, management structure, procedures and tools, reporting guidelines, financial and budget issues, as well as guidelines for dissemination and communication of results.

D2.2 – Communication and dissemination strategy plan (LYN, M3)

Document illustrating the SIMCor communication and dissemination strategy plan, along with the relevant activities carried out in the first three months of project implementation.

D1.2 – Kick-off meeting report (LYN, M2)

Report summarising the project kick-off meeting, including general overview, work package presentations, working groups and discussions.

D2.1 – Project presentation (LYN, M1)

Description of project rationale, mission and objectives, consortium composition and role of partners, implementation and expected impacts in plain language, for communication and dissemination purposes.

Mapping the use of computer modelling and simulation in clinics

Report of the 1st VPHi Clinical Community Survey.

Demo of the Virtual Research Environment (VRE). This video demonstrates the usability of the Virtual Cohort Generators that were embedded into the SIMCor Virtual Research Environment.

R-Statistical Environment embedded in the SIMCor Virtual Research Environment. The video shows how RStudio allows to run statistical evaluations in a standardised manner directly on the SIMCor Virtual Research Environment (VRE).

Clinically driven Virtual Cohort Generator for aortic valve geometries. This video demonstrates the generator for virtual geometries of the aortic rude, including the aortic valve leaflets based on clinically driven features.

Demo of TAVI deployment simulations using the virtual geometries and a high-fidelity model. This video demonstrates the feasibility of performing finite element TAVI deployment simulations in the virtual patient geometries generated using the Virtual Cohort Generator.

Demo of fast-to-evaluate surrogate models used for device implantation. This video illustrates a fast-to-evaluate model for TAVI device implantation based on isogeometric analysis.

SIMCor Pills – Ep.6 | Healthcare and socioeconomic impacts of in-silico trials.

Christian Ohman from the European Clinical Research Infrastructure Network (ECRIN) and Thomas Czypionka from the Institute for Advanced Studies (IHS) of Vienna illustrate the potential benefits of in-silico clinical trials and healthcare and socioeconomic level.

SIMCor Pills – Ep.5 | Modelling and simulation for pulmonary artery pressure sensors (PAPS).

Andreas Arndt from Biotronik explains what a pulmonary artery pressure sensor (PAPS) is and how in-silico investigations of the interaction between the sensor, the vessel wall and the blood flow during and post-implantation can help avoid or reduce the risk of post-operative complications like perforation, migration and thrombosis.

SIMCor Pills – Ep.4 | Tissue properties investigation for vessel modelling.

Malte Rolf-Pissarczyk from Graz University of Technology (TU Graz) explains how to create vessel models of the ascending aorta and pulmonary artery using biaxial extension tests and microstructural investigations of animal and human tissues, that can be used for modelling and simulation of migration and deployment of medical devices.

SIMCor Pills – Ep.3 | Modeling and simulation for transcatheter aortic valve implantation (TAVI)

What is an in-silico simulation for assessing transcatheter aortic valve implants (TAVI) and what are they used for? Michael Stiehm from Institut für Implantattechnologie und Biomaterialien e.V. (IIB), and Michelle Spanjaards from Philips Netherlands (PHI), explain how in-silico simulation of device implantation and assessment of the blood blow after implantation can help plan surgical replacement procedures at best and help reduce post-operative complications, such as thrombosis.

SIMCor Pills – Ep.2 | Virtual patient cohorts

What is a virtual patient, and a virtual patient cohort? Wouter Huberts from the Eindhoven University of Technology (TUE) explains our virtual cohort generation and validation methodology, passing through its 3 validation steps: (1) patient-level validation, (2) self-validation and (3) cross-validation.

SIMCor Pills – Ep.1 | SIMCor: what is it about?

Titus Kühne and Jan Brüning, Project Coordinator and Deputy Coordinator from the German Heart Centre of the Charité – Universitätsmedizin Berlin (CHA), illustrate the rationale and scope of our project, explaining our approach of using in-silico trials for assessing the safety and efficacy of our medical device use cases, TAVI and PAPS.

SIMCor: an overview 

This 13′ collection of interview clips, released at the end of its 42-month activity, offers a comprehensive overview of its objectives, methods, results and foreseen impacts narrated by the team leader of each consortium partner.

SIMCor: an overview | Trailer

This 3′ collection of interview clips represents the short version of ‘SIMCor: an overview’, providing a high-level overview of its objectives, methods, results and foreseen impacts narrated by the team leader of each consortium partner.

Code & Cure: Understanding In Silico Medicine | Episode 4: Technologies behind in silico medicine

In silico medicine wouldn’t be possible without many supporting technologies. The fourth video of the series Code & Cure will present how medical imaging, supercomputers, and virtual and augmented reality (AR/VR) contribute to it.

Code & Cure: Understanding In Silico Medicine | Episode 3: Artificial Intelligence in healthcare

As an in silico medicine tool, Artificial Intelligence plays an important role in many aspects of medical clinics. In this video, we will show how, thanks to AI, we can turn all the data surrounding us into actionable medical decisions.

Code & Cure: Understanding In Silico Medicine | Episode 2: What is a computer model?

You might not notice it, but computer models are everywhere around us, from your smartphone calculating the fastest route to work to the aerodynamic simulations of cars and planes. In this video, we will discover how we can make the most of them for a better healthcare.

Code & Cure: Understanding In Silico Medicine | Episode 1: How computers are changing healthcare

What is the next big thing in healthcare? In silico medicine, of course. But what is it? Let’s discover it together with the series “Code & Cure: Understanding In Silico Medicine”. This video explains in a very simple way the concept of in silico medicine and its benefits for everyone.

Poster pitch video | Virtual cohort generation for in silico trials of transcatheter aortic valve implantation. S. C. F. P. M. Verstraeten, M. J. M. M. Hoeijmakers, F. N. van de Vosse, W. Huberts. Presented at the 28th Annual Congress of the European Society of Biomechanics (ESB2023), Maastricht, 9-12 July 2023.

Pills of in-silico medicine Ep.4 | In silico trial perspectives

Fourth episode of the series dedicated to in-silico medicine

Pills of in-silico medicine Ep.3 | In-silico clinical trials for device testing

Third episode of the series dedicated to in-silico medicine

Pills of in-silico medicine Ep.2 | Virtual cohorts

Second episode of the series dedicated to in-silico medicine

Pills of in-silico medicine Ep.1 | In-silico medicine

First episode of the series dedicated to in-silico medicine

SIMCor: in-Silico testing and validation of Cardiovascular IMplantable devices

Video teaser of the project

Horizon 2020 Research and Innovation Action kick-off: SIMCor (In-Silico testing and validation of Cardiovascular IMplantable devices)

Press release n.1/2021 of the SIMCor project, announcing project inception and conclusion of our kick-off meeting.

SIMCor: an overview

Leaflet illustrating project rationale, objectives and clinical focus.