Dr. phil. Richard McKinley

Stroke treatment goes personalized: Gaining added diagnostic yield by computer-assisted treatment selection (STRAY-CATS)

In this DACH project together with the Technical University of Munich we aim to extend and validate our existing FASTER model of stroke tissue outcome in mechanical thrombectomy, validating on data provided by our partners at the University of California Los Angeles, and incorporating modelling of collateral flow provided by researchers at the Technical University of Munich.

Advanced Stroke Analytics Platform (ASAP)

In this Innosuisse funded project, together with the CHUV radiology and Siemens Healthineers (Advanced Clinical Imaging Technology group), we aim to build advanced models for perfusion image processing and predictive analytics based on clinical variables and acute imaging, using data from patients treated at the CHUV and the Insel Stroke Unit.  Learning is based on a federated data model, where the data remains inside the participating institutions, rather than being shared.  The major challenge of the project is to handle the heterogeneity of data between the institutions, so that subsequent tools based on the data can be applied to data from a third institution.

Research fields

• Stroke imaging
• Endovascular stroke treatment
• Machine Learning in acute stroke