Clinnova Teams

THE TEAMS

Clinnova Multiple Sclerosis (MS) team

Prof. Cristina Granziera

Principal Investigator,
Clinnova Basel co-CEO of RC2NB

Dr. Bebeka Cosandey

Lead Scientific Project Manager

Tanja Stoll

Study nurse

Between 2024 and 2025, the Clinnova-MS study reached several major milestones, paving the way for its long-term research on multiple sclerosis (MS). After receiving Swiss ethics approval in June 2024 and registering on ClinicalTrials.gov the following month (NCT06526364), the study began setting up clinical sites and preparing for patient enrollment. A key innovation was the integration of the digital dreaMS app—a gamified, remote monitoring tool that tracks cognitive and functional changes in MS patients—advancing the vision of decentralized, real-world data collection.

To ensure smooth operations, a dedicated study nurse joined the team in August 2024, standardizing procedures and overseeing data quality. Rigorous laboratory dry runs validated all protocols before the first participant joined. Recruitment began with a targeted strategy, leading to the First Patient In (FPI) at the University Hospital Basel in December 2024—a defining milestone for the project. The Clinnova consortium also launched its official website in February 2025 to share progress and foster collaboration across the research community.

By August 2025, over 20 participants had been enrolled, with a consistent recruitment rate of two to three patients per month. Early results show high engagement, with dreaMS app adherence reaching 85%, reflecting the tool’s accessibility and patient motivation. Many participants also volunteered optional biological samples, reinforcing their commitment to advancing MS research and contributing to a robust, high-quality study cohort.

Beyond clinical progress, the study’s imaging and biomarker work packages are advancing scientific excellence. Dedicated staff are setting up centralized imaging data systems and developing analytical pipelines that feed into Clinnova’s federated learning framework. In parallel, biological samples—blood, cerebrospinal fluid, and stool—are collected and processed at clinical sites before being transferred to Luxembourg’s biobank. Together, these efforts establish a powerful foundation for discovering new biomarkers and driving personalized approaches to MS treatment.

Blood sample process

Clinnova Inflammatory Bowel Disease (IBD) team

Prof. Jan Niess

Principal Investigator

Prof. Dr. med. Petr Hrúz

Principal Investigator

Véronique Pflimlin-Fritschy

Study nurse

Katline Metzger-Peter

Study nurse

The Clinnova-IBD use case is one of three clinical applications within the Clinnova consortium, designed to improve the management of Crohn’s disease and ulcerative colitis through standardized protocols, interoperable data systems, and cross-border collaboration. By harmonizing clinical documentation and digital tools, the initiative supports the development of personalized, data-driven care strategies tailored to patients’ diverse needs.

At the Basel site, led by Prof. Jan Niess (University Hospital Basel) and Prof. Petr Hrúz (Claraspital), the Clarunis centers play a central role in implementing Clinnova’s shared vision. Between 2024 and 2025, the Basel team achieved major milestones: ethics approval for the local IBD protocol, a bilateral Material and Data Transfer Agreement with the Luxembourg Institute of Health, and comprehensive site training to test and refine operational workflows. These efforts have strengthened readiness across both hospitals and fostered collaboration within the broader Clinnova network.

Data management is a cornerstone of the project. Clinical information is securely recorded in REDCap using standardized terminologies such as CDISC and SNOMED CT, while patient-reported outcomes are collected via the Colive app, ensuring high-quality, pseudonymized data and compliance with data protection standards. Looking ahead, the Basel team will focus on patient recruitment, data integration, and exploratory analyses to identify clinically relevant patient subgroups. These next steps aim to establish interoperable, FAIR-compliant data infrastructures that will drive innovation in personalized care for inflammatory bowel disease and beyond.

Figure: Participant Survey Interface and Questionnaire in the Colive App - Colive app is a s useful tool for remote diagnosis, prevention and monitoring of diseases developed by Luxembourg Institute of Health. a) The first panel displays the participant survey interface, showing pending surveys with their status, title, date range, and progress, allowing users to access and complete them directly. b) The second panel shows an example of the Short Form Dietary Questionnaire, illustrating the questionnaire interface and progress indicator.

Clinnova Inflammatory Bowel Disease (IBD) team

Dr. Bram Stieltjes

Principal Investigator,
Data Architecture Lead, Clinnova Basel

Francesco Santini

Development Lead Engineer for Federated Learning - Medical Imaging

Jessica Schäper

Development Engineer for Federated Learning - Medical Imaging

Siri Leemann

Project Manager Personalized Health Basel

Clinnova-FL enables hospitals and research institutions to collaboratively train AI models without sharing sensitive patient data. Using federated learning, data stays securely within each institution while only the learned model parameters are exchanged. This approach protects privacy and builds trust, addressing a major challenge in healthcare AI. The initial focus is on detecting and analyzing brain lesions in multiple sclerosis (MS) patients, with the long-term goal of expanding to other diseases and imaging challenges.

At its core, Clinnova-FL represents a new model for collaboration: moving algorithms instead of data. Led by the Basel team, the project combines advanced AI with strict data protection and regulatory standards. The goal is to create a “learning healthcare system” where institutions jointly improve AI models, accelerating medical innovation while maintaining full data control.

Clinnova’s broader vision is to build a Federated Network of Data Integration Centres—an interoperable infrastructure allowing hospitals to collaborate securely. Based on the Dafne ecosystem, the system is scalable, modular, intuitive for clinicians, and compliant with clinical and legal standards.

Technically, Clinnova-FL integrates the Dafne platform with NVIDIA FLARE for secure model training and aggregation. Each site trains models locally, exchanging only model weights. The architecture, built on a UNet model with preprocessing and postprocessing steps, ensures ready clinical deployment. This combination of usability, scalability, and privacy positions Clinnova-FL as a foundation for ethical, collaborative AI in healthcare.