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Data-driven cardiovascular assist devices

Objective
We propose to use hybrid testing to innovate data-driven solutions for cardiac assistance. The project aims to enable data-driven evaluation of novel cardiac support devices to allow a rich and healthy life for patients with cardiovascular disease.

Background
We are currently witnessing an epidemic of heart failure with a rising incidence in the general population worldwide (2–7%) and a mean survival of only five years. The last decade has seen tremendous advances in device-based treatment options. However, progress has stalled, with only one device being currently approved for use in humans.

At the same time, novel hybrid mock circulation loops have been developed, allowing physical device interaction with a digital model of the human cardiovascular system. Here at KTH, we built Sweden’s first cardiovascular hybrid mock circulation. In this way, we can mimic unprecedented amounts of virtual and physical implantations of potential candidates of cardiac assistive technologies. The hope is that machine learning approaches can aid in identifying the ideal position and actuation profile of the cardiac assist device of the future.

Crossdisciplinary collaboration
The researchers in the team represent the KTH School of Electrical Engineering and Computer Science and the School of Chemistry, Biotechnology, and Health.

Contacts

Picture of Stefan Bauer

Stefan Bauer

Assistant Professor at KTH EECS, Working group Learn, Co-PI: Data-driven cardiovascular assist devices, Former Co-PI: Seed funding for large grant proposals, Digital Futures Faculty

baue@kth.se

Seraina Dual

Assistant Professor at the Division of Health Informatics and Logistics at the CBH School at KTH, Co-PI of project Data-driven cardiovascular assist devices, Digital Futures Faculty

+46 8 790 97 69
seraina@kth.se