Lotte Fleurkens-Ewals

Catharina ziekenhuis Eindhoven

In short, the fellowship of Lotte Fleurkens-Ewals entails the following:

Integration of AI-assisted tools into clinical practice to enhance imaging-based evaluation of periampullary and peritoneal cancers

Periampullary and peritoneal cancers are highly lethal diseases. Detecting periampullary tumors on CT scans requires specific expertise and tumors may be missed. To address this, the Eindhoven University ofTechnology and Catharina Hospital Eindhoven developed an AI model that detects periampullary tumors on CT scans. For peritoneal cancer, accurately assessing the tumor burden is crucial for determining treatment eligibility and evaluating response. The Peritoneal Cancer Index (PCI), which scores the extent of cancer across 13 abdominal regions, is an essential prognostic tool, typically assessed through diagnostic laparoscopy. To provide a non-invasive alternative, an AI model has been developed to automatically segment the PCI regions on CT scans, supporting a structured and quantitative assessment on imaging. While both models have demonstrated promising performance in pre-clinical validation,  they require prospective clinical validation to confirm their true added value in clinical practice.

During this fellowship, Lotte Fleurkens-Ewals, Technical Physician, will focus on the clinical implementation of these AI models. The fellowship will involve integrating these two models into clinical workflows, ensuring seamless interaction between AI and radiologists, and evaluating their feasibility and clinical value. This will include collaboration with multidisciplinary teams of AI developers, radiologists, and clinical experts, as well as conducting proof-of-concept studies at the Catharina Hospital and other hospitals. To enhance expertise in AI integration, she will participate in relevant courses and undertake an internship at the University of Toronto, contributing to the implementation of an AI-based triage tool for thyroid disease in Costa Rica. The goal of this fellowship is to not only advance the clinical use of these AI models but also to lay the groundwork for future AI-assisted diagnostic imaging tools.