In this PhD project, we will investigate novel AI methods for application in stroke interventions .The main challenges to be addressed are improving image guidance by including pre-operative image information, and models to assess procedural result and predict outcome. By reducing complexity from the information source during the procedure, outcome can be optimized. Furthermore, a realtime analysis of all the pre- and intra-operational information sources (images, intra-body measurements) by AI methods and a translation to the current patient can contribute to treatment success.

This PhD project is one of the five PhD positions in the ICAI Stroke Lab, which is one of the labs in the ROBUST program. The ROBUST program on Trustworthy AI-based Systems adds 17 new labs to the ICAI's current ecosystem.

The aim of the ICAI Stroke lab is to improve the outcome of stroke patients by optimizing the whole chain from the 112-call to rehabilitation with AI. Specifically, we intend to develop and assess AI-based patient stratification (faster, more accurate) for acute stroke care interventions, AI based functional outcome prediction after endovascular therapy, image-guidance in endovascular interventions for better therapeutic outcome and data-driven individualized stroke recovery policies, based on patients' current abilities and on constantly updated prognostic models for stroke recovery. In addition, we will study the acceptance of such AI-models by clinical decision-makers. There will be strong collaboration between the PhD students in the ICAI lab.