Stratification, Management, and Guidance of Hypertrophic Cardiomyopathy Patients using Hybrid Digital Twin Solutions

Project Information

SMASH-HCM

Grant agreement ID: 101137115

DOI

10.3030/101137115

EC signature date 8 December 2023

Start date 1 January 2024

End date 31 December 2027

Funded under

Health

Total cost

€ 8 546 366,25

EU contribution

€ 8 546 366,25

8 546 366,25

Coordinated by

TAMPEREEN KORKEAKOULUSAATIO SR
Finland

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Quality assurance plan

Documents standards, processes and ways of working

Risk assessment reports

Risk and quality assessment

Requirements for disease patients' self-management

Requirements spec. for patient view of disease self-management

SMASH-HCM solution as research tool requirements

Technical and non-technical specification for the tool in a continued research setting

Dissemination, communication and exploitation plan

Strategy documents

Ethical approval of the whole SMASH-HCM study

Approval distributed to centers collecting clinical data

Initial Data-driven models

Data-driven models and biomarkers available

Design specifications of an API for DSS algorithms

API’s functionality is specified

Initial Evidence-based models and biomarkers

Evidence-based models and biomarkers available

VASC Submission package

Ethical approval and EU clinical trials registration

Libraries of HCM populations of cellular electromechanical models
Requirements SMASH-HCM solution in clinical practice – initial

Technical and non-technical requirements for the SMASH-HCM solution

PILOT Submission package

Approvals by all clinical partners

Governance report

Specifies decision-making processes and project-related bodies’ members

Requirements SMASH-HCM predictive/clustering tool

Requirement specifications for predictive/clustering tool for HCM patient stratification

Publications

Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing

Author(s): Julia Camps, Zhinuo Jenny Wang, Ruben Doste, Lucas Arantes Berg, Maxx Holmes, Brodie Lawson, Jakub Tomek, Kevin Burrage, Alfonso Bueno-Orovio, Blanca Rodriguez
Published in: Medical Image Analysis, Issue 100, 2024, ISSN 1361-8415
Publisher: Elsevier BV
DOI: 10.1016/J.MEDIA.2024.103361

Artificial intelligence applications in hypertrophic cardiomyopathy: turns and loopholes

Author(s): Giorgia Panichella, Manuel Garofalo, Laura Sasso, Alessandra Milazzo, Alessandra Fornaro, Josè Manuel Pioner, Alfonso Bueno-Orovio, Mark van Gils, Annariina Koivu, Luca Mainardi, Virginie Le Rolle, Felix Agakov, Maurizio Pieroni, Katriina Aalto-Setälä, Jari Hyttinen, Iacopo Olivotto, Annamaria Del Franco
Published in: European Heart Journal - Digital Health, 2025, ISSN 2634-3916
Publisher: Oxford University Press (OUP)
DOI: 10.1093/EHJDH/ZTAF086

Digitaalisen kaksosteknologian hyödyntäminen terveydenhuollossa: Hyödyt ja vaikutukset hypertrofisen kardiomyopatian tapaustutkimuksen valossa

Author(s): Annariina Koivu, Mark Van Gils, Antti Ahola, Katriina Aalto-Setälä, Jari Hyttinen
Published in: Finnish Journal of eHealth and eWelfare, Issue 17, 2025, ISSN 1798-0798
Publisher: Finnish Social and Health Informatics Association
DOI: 10.23996/FJHW.154831

An ECG-Based Model for Left Ventricular Hypertrophy Detection: A Machine Learning Approach

Author(s): Marion Taconné; Valentina D.A. Corino; Luca Mainardi
Published in: IEEE Open Journal of Engineering in Medicine and Biology, 2024, ISSN 2644-1276
Publisher: IEEE
DOI: 10.1109/OJEMB.2024.3509379

Defining functional requirements for a self-management solution for Hypertrophic Cardiomyopathy patients – a multi-stakeholder multi-framework approach

Author(s): Kati Auranen, Dipak Kalra, Malte Von Tottleben, Shahan Tariq, Mark Van Gils
Published in: Finnish Journal of eHealth and eWelfare, Issue 17, 2025, ISSN 1798-0798
Publisher: Finnish Journal of eHealth and eWelfare
DOI: 10.23996/FJHW.154635

Multilevel modelling for decision support in hypertrophic cardiomyopathy in the SMASH-HCM project

Author(s): Alpo Olavi Värri; Mark Van Gils; Jari Hyttinen
Published in: Finnish Journal of eHealth and eWelfare, 2024, ISSN 1798-0798
Publisher: Finnish Social and Health Informatics Association
DOI: 10.23996/FJHW.143516

Evaluating Convolution, Attention, and Mamba Based U-Net Models for Multi-class Bi-Atrial Segmentation from LGE-MRI

Author(s): Claas Thesing, Alfonso Bueno-Orovio, Abhirup Banerjee
Published in: Lecture Notes in Computer Science, Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers., 2025, ISBN 978-3-031-87756-8
Publisher: Springer Nature Switzerland
DOI: 10.1007/978-3-031-87756-8_22

Automatic Right Ventricular Hypertrophic Detection Integrating Electrocardiography-based QRS Biomarkers with Machine Learning

Author(s): Marion Taconne, Valentina Corino, Luca Mainardi
Published in: Computing in Cardiology Conference (CinC), 2024 Computing in Cardiology Conference (CinC), Issue 51, 2024, ISSN 2325-887X
Publisher: Computing in Cardiology
DOI: 10.22489/CINC.2024.085

Model-based Analysis of Myocardial Strains in Obstructive and Non-obstructive Hypertrophic Cardiomyopathy

Author(s): Francesca Menna, Joan Duprez, Marion Taconne, Adrien AL WAZZAN, Eleonore SERRANO, Lotfi Senhadji, erwan donal, Jari A. Hyttinen, Alfredo Hernandez, Virginie Le Rolle
Published in: Computing in Cardiology Conference (CinC), 2024 Computing in Cardiology Conference (CinC), Issue 51, 2024, ISSN 2325-887X
Publisher: Computing in Cardiology
DOI: 10.22489/CINC.2024.240

Integrated Requirements Engineering for Digital Health: Enhancing Self-Care in Hypertrophic Cardiomyopathy Management

Author(s): Kati Auranen
Published in: 2024
Publisher: Tampere University

Searching for OpenAIRE data...

Deliverables

Publications

Download Download the content of the page