AISN project achievements

A user experience study was conducted in four European countries between May and December 2024. Four clinical partner centres were involved: San Camillo (Italy), Parc Sanitari Sant Joan de Déu (Spain), the University of Limoges (France) and the University of Medicine and Pharmacy of Cluj-Napoca (Romania). Each centre conducted at least three separate focus groups composed of patients, healthcare professionals (therapists and clinicians) and carers.

The consortium evaluated feedback from AISN stakeholders on the early version of the AISN platform. The 71 participants enrolled evaluated key tools of the AISN system, including: the Rehabilitation Gaming System (RGS), a motor and cognitive rehabilitation platform; the Medical Information Management System (MIMS), a medical information platform integrating decision support for clinicians; the BrainX3, a neuroinformatics tool for analysing brain data; the Neurorehab educational platform, a website that provides educational information on stroke and post-stroke rehabilitation.

Quantitative and qualitative evaluations of the focus groups suggest an overall positive evaluation of the basic model of the AISN system. The System Usability Scale (SUS) of C (Marginal) indicated a reasonable usability of the technology explored during the activities, with room for improvement. This rating indicates that although the System has a basic level of functionality and accessibility, improvements are needed to better meet user expectations and ensure a seamless experience. Healthcare professionals and carers rated the system higher than patients, with the highest usability scores reported in Spain and the lowest in Romania. Strengths of the system included its intuitive design, adaptability and potential to enable effective remote rehabilitation. However, challenges were identified, including System complexity, a steep learning curve for users with lower digital literacy, and concerns about over-reliance on AI, which could affect trust and overall usability. Motivation and engagement were emphasized as crucial factors, with suggestions such as gamification, personalized feedback, and clear instructions to support better adherence. Human support was also seen as vital, with a strong call for clinical oversight and transparent AI-based recommendations under professional supervision.

These findings will guide the next phase of the AISN project, including updating the RGSapp and AI-based clinician decision support prior to clinical validation through the AISN randomised controlled trial (RCT).

AISN has focused on scientific advancements and developing signal processing toolboxes and computational modeling. The team implemented a whole-brain Wilson-Cowan model to study brain dynamics post-lesion, validated with EEG and fMRI data from stroke patients. They analyzed EEG data, quantified metastability over time, and correlated it with recovery using clinical scales, identifying metastability and synchrony as potential biomarkers. Preliminary analysis of RGS@home (RGS: Rehabilitation Gaming System) intervention data evaluated progress, revealing covariate effects on clinical scores. The team developed pipelines to extract kinematic variables correlated with clinical scales and deployed a Docker-based infrastructure with a Vector DB and FastAPI for a recommender engine, planning to validate it with real clinical data. They advanced RGS solutions by improving RGSapp with new features like video tutorials, a more interactive user interface, and enhanced messaging systems with machine learning-driven personalized coaching. Efforts to improve cybersecurity included upgrading servers and optimizing data transfer methods. The integration of the algorithm for diagnosis and prognosis on the server has been successful. Improvements in RGSweb protocol development, RGSwear's user interface, and data transfer reliability were achieved. Accessibility enhancements included adding Romanian language support and creating user manuals. The Medical Information Management System (MIMS) platform now offers personalized patient experiences and preliminary diagnostic outputs. AISN developed a robust AI pipeline for patient prognosis, using statistical models for interpretable AI algorithms. The prognosis module, integrated into the AISN system, supports real-time prediction and data storage, enhancing clinical trials' prognostic predictions and providing insights for clinicians. The project developed standards for structuring computational modeling data, applied existing standards to multimodal brain imaging data, and organized and annotated stroke datasets accordingly. These datasets are openly discoverable via EBRAINS KG, with guidelines for GDPR-compliant data sharing.

Beyond state of the art

The AISN project has identified metastability and synchrony as potential biomarkers for stroke recovery, enhancing the understanding of brain dynamics post-lesion. The detailed examination of RGS@home intervention data provides insights into key factors affecting recovery and suggests directions for future improvements. Pipelines for extracting kinematic variables allow for clinical scale-free assessments of recovery. The deployment of a Docker-based infrastructure for a recommender engine is a substantial technological advancement, with the potential to significantly impact personalized healthcare solutions. The advancements in RGS solutions, such as personalized and interactive elements in RGSapp, combined with enhanced cybersecurity and reliable data handling, ensure robust and secure user experiences. The successful integration of the algorithm marks a step forward in personalized healthcare, with potential for improved diagnostic and prognostic capabilities. Continued improvements in RGSweb and RGSwear enhance the usability and accuracy of the solutions, while the personalized features of the MIMS platform address patient needs directly. AISN has achieved advancements in patient prognosis and clinical decision support. By integrating the prognosis module into the AISN system, real-time prediction and data storage enhance clinical decision-making efficiency and accuracy. Next steps are to conduct more research and data collection from diverse sources, improve the prognosis model's accuracy, and add new features to the decision support module. This involves continuous platform maintenance, additional data gathering, and model refinement. By extending features, optimizing AI modules for scalability, and ensuring efficient and secure operation, we advance clinical decision support. Charité’s standardized stroke patient datasets, processing workflows, GDPR ready infrastructure, and guidelines for data sharing benefit the European and international research community, creating an impact beyond the project.