Annekoos Schaap is currently a PhD candidate within the research group Electronic Systems at the department of Electrical Engineering at the TU/e, where she is supervised by part-time professor Danny Ruijters and full professor Sveta Zinger. With a master's degree in Electrical Engineering specializing in Signal Processing Systems and Care & Cure from the TU/e, Annekoos Schaap has a strong foundation in AI-driven medical imaging. Her master’s thesis focused on prostate cancer prognosis, integrating diagnostic imaging and genomic data to identify imaging features associated with tumor aggressiveness. Using machine learning models (SVM, KNN, LR), she classified tumors based on their Gleason scores. Her research highlighted significant correlations between imaging and genomic features, supporting non-invasive personalized treatment strategies.

Advances in medical imaging, AI, and machine learning have the potential to revolutionize healthcare. This research focuses on developing and implementing AI-powered image processing techniques to streamline treatment for cerebral aneurysms. By leveraging state-of-the-art machine learning algorithms, the goal is to reduce strain on healthcare personnel.

By implementing AI-assisted procedural guidance, we want to enhance minimally invasive interventions, and workflow automation to reduce cognitive and physical workload for medical staff. By integrating AI-driven solutions into clinical practice, this research aims to shorten reduce procedural time, reduce X-ray exposure and improve overall efficiency.

A critical aspect of this work is ensuring the usability and reliability of AI-assisted tools in real-world medical settings. Therefore, reader studies will be conducted to assess the effectiveness and clinical impact of the developed techniques. By focusing on both patient care and healthcare personnel well-being, this research contributes to a more efficient, accurate, and patient-centered healthcare system.