Peer-review publications

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 analysisInternational Journal of Computer Assisted Radiology and Surgery17(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.

Public Deliverables

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.

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.

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

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

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.

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.

D7.3 – 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.

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.

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.

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.

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.

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.

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.

Press Releases

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.

Leaflet

Leaflet

Leaflet illustrating project rationale, objectives and clinical focus.