2nd progress meeting of the REDDIE project @UNIPD

In the past few days, we had the pleasure of hosting the second progress meeting of the Horizon Europe REDDIE project at our Department of Information Engineering. Over two days of discussions, we reviewed progress across work packages, shared insights on emerging results, and conducted workshops on key challenges within each WP.
It was a fantastic opportunity to welcome our international partners, exchange ideas in person, and strengthen collaborations—while also enjoying good food and the first signs of spring in Padua! 😊

2nd Annual Meeting of the DARE Initiative

On 20-21 February 2025, the Istituto Ortopedico Rizzoli in Bologna hosted the 2nd Annual Meeting of the DARE – Digital lifelong prevention initiative – Spoke 3 on Digitally Enabled Secondary and Tertiary Prevention. The event gathered researchers, clinicians, and experts working on innovative digital health solutions to enhance disease prevention and early diagnosis.
Our researcher Enrico Longato presented our latest work within Work Package 3 (WP3), focusing on the development of AI-driven digital tools for kidney disease prediction in diabetes. These tools leverage data and computational models to support personalised and data-driven healthcare approaches, aligning with the broader goals of the DARE Initiative.
We are proud to contribute to this important initiative and to collaborate with a multidisciplinary network of researchers committed to advancing digital health. The meeting provided a valuable opportunity for discussions, knowledge exchange, and new perspectives on integrating AI into clinical workflows.
We look forward to further developments and continued collaboration within the DARE Initiative.

Building Bridges Between Engineering and Medicine Through AI

On Tuesday, 21 January 2025, the Aula Morgagni at the Policlinico Universitario of Padua hosted the event “Introduction to Artificial Intelligence in Medicine.” Organised by the Departments of Information Engineering (DEI) and Medicine (DIMED), the meeting brought together experts and researchers to discuss the transformative role of AI in healthcare.

The session was opened by Professors Gaudenzio Meneghesso, Director of the DEI, and Paolo Simioni, Director of the DIMED, who welcomed attendees and highlighted the interdisciplinary collaboration driving AI innovation.

The scientific programme included a series of engaging presentations:

  • Introduction to Machine Learning by Prof. Barbara Di Camillo (DEI) provided a comprehensive overview of the fundamentals of machine learning and its applications in medical research.
  • Applications to Clinical Records by Dr. Erica Tavazzi (DEI), a researcher from our group, explored AI-driven approaches to extracting insights from patient data.
  • Applications to Imaging Data by Dr. Marco Castellaro (DEI) demonstrated how AI enhances diagnostic imaging techniques.
  • Large Language Models and Generative AI and Chatbots were expertly discussed by Prof. Giorgio Satta (DEI), who highlighted their potential to revolutionise communication and support activities.
  • Data Representation and Knowledge in Generative AI by Prof. Nicola Ferro (DEI) delved into the technical challenges and opportunities in knowledge-based systems.

The event concluded with a dynamic Q&A session, fostering lively discussions among attendees and speakers.

Showcasing Progress in Digital Health Research at the DARE Initiative Meeting

On 15 January 2025, the Aula Magna of the Department of Information Engineering hosted a meeting dedicated to the research efforts of UNIPD within Spokes 2 and 3 of the DARE (Digital Life-Long Prevention) initiative.

During the event, Professor Barbara Di Camillo presented the pilot project in which our research group is actively involved, titled “Digitally-Empowered Management of Type 2 Diabetes: From the Diagnosis to the Prediction of Complications.”

It was a privilege to contribute to this gathering, which highlighted the exceptional quality and scientific relevance of the ongoing projects within the DARE initiative. The exchange of ideas and insights reaffirmed the initiative’s potential to make significant strides in digital health research.

Unveiling the Impact of Pollutants and Weather Patterns on Relapses in Multiple Sclerosis: Insights from BRAINTEASER Presented at IEEE BIBM 2024

Last week, our PhD student Elena Marinello presented her research titled “Machine Learning Models Highlight the Impact of Pollution and Weather Patterns on Relapse Occurrence in Multiple Sclerosis Patients” at the Artificial Intelligence and Computational Methods for Public Health and the Environment workshop organised as part of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2024.
This study, conducted as part of the H2020 BRAINTEASER project, investigates how environmental factors, including pollution and weather patterns, contribute to predicting imminent relapses in multiple sclerosis. By leveraging clinical and environmental data from the week preceding potential relapse events, this research highlights the role of machine learning in advancing personalised medicine for MS patients.

BRAINTEASER Research Highlighted at LOD2024

Last week, our post-doc Alessandro Guazzo attended the 10th International Conference on Machine Learning, Optimization, and Data Science held in Castiglione della Pescaia, Italy. 

Alessandro gave a presentation on his work “Predicting Multiple Sclerosis Worsening Using Stratification-Based and Time-Dependent Variables Extracted from Routine Visits Data” developed within the BRAINTEASER project, focusing on improving the prediction of multiple sclerosis worsening. His talk highlighted innovative stratification-based variable extraction approaches, which he had the opportunity to study during his recent visit to Carnegie Mellon University. 

Participation in the Brain Innovation Days 2024 in Brussels

The 4th edition of the Brain Innovation Days, organised by the European Brain Council, will take place on 13-14 November 2024 in Brussels, Belgium, under the overarching theme “Navigating the Brain Across a Lifetime“.
This event aims to bring the wider brain ecosystem together to foster dialogue, exchange knowledge, accelerate investment in research & innovation, and facilitate business development.

The main theme will revolve around 5 subthemes:

🧠 Blossoming Brains: Early Brain Development
🏫 Building Brains: Schools and Workplaces
🏥 Timeless Brains: Nurturing Resilience, Embracing Change
⚙️ Holistic Brains: Strategies for Brain Health in a Dynamic Society
🚀 Advancements in Neurotechnology: Pioneering Innovations

The programme will include a wide array of session types, including plenary sessions, inspiring Brain Talks, Poster and Innovation Showcase, matchmaking and networking activities, the Brain Innovation Days Pitch Competition, breakout sessions, panels, and how-to sessions.

Our PI Prof. Barbara Di Camillo will take part in the “Harnessing AI for brain health: lab to market to society” panel, an event that marks the conclusion of the BRAINTEASER project, celebrating its advances in remote prediction, prevention, and care for people with multiple sclerosis (MS) and amyotrophic lateral sclerosis (ALS). Key sessions will cover the transition of BRAINTEASER’s technology (an app and wearables) from the lab to the market, and the potential impact on patient-centred care. There will also be a showcase of the project’s clinical app and insights on its application to other neurodegenerative diseases like Parkinson’s.

The Brain Innovation Days offer an exciting opportunity to engage with the latest innovations in brain health, and we are proud to see Prof. Di Camillo contribute to the discussion on AI’s role in advancing care from lab to market, reinforcing our commitment to impactful, patient-centred research.

8th Plenary Meeting of the BRAINTEASER Project in Turin, 19-20 September 2024

The 8th plenary meeting of the BRAINTEASER project is currently taking place in Turin (19-20 September 2024). During these two days, all project partners are presenting their updates across the 12 work packages. Our group leads Work Package 7 (WP7), focused on “AI models for disease progression.” The objectives of WP7 include developing models to predict disease progression in amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS), incorporating new variables at various individual levels that are currently underutilised in the literature, validating these models on external datasets, and using them to create in-silico patient simulators.

In recent months, our team has been working on a task aimed at developing an in-silico patient simulator capable of modelling and simulating disease evolution, specifically focusing on changes in patients’ independence in domains affected by ALS and MS. Today, our post-doc Erica Tavazzi presented the results of these efforts. The models, based on dynamic Bayesian networks and developed using dynamic clinical data from ALS and MS patients, have successfully captured the relationships between key variables over time, allowing for a detailed simulation of disease progression. Moreover, we demonstrated the models’ effectiveness in evaluating the impact of specific biomarkers on disease progression and the efficacy of preventive treatments.

We extend our sincere thanks to the Department of Neuroscience at the University of Turin for hosting and organising this meeting. We are proud to be part of the BRAINTEASER consortium, which aims to harness advanced AI technologies to improve the management and understanding of neurodegenerative diseases, ultimately enhancing patients’ quality of life.

Improving ALS and MS Progression Prediction with Wearable and Environmental Data: Our Contribution to CLEF 2024

Last week, we had the pleasure of participating in the 15th Conference and Labs of the Evaluation Forum (CLEF), held from 9-12 September in Grenoble, France. As part of the iDPP Challenge, we presented our work titled “Using wearable and environmental data to improve the prediction of amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) progression: an exploratory study” (E. Marinello, A. Guazzo, E. Longato, E. Tavazzi, I. Trescato, M. Vettoretti, B. Di Camillo), developed within the BRAINTEASER H2020 project. The paper, included in the CLEF 2024 Working Notes, can be accessed here: https://ceur-ws.org/Vol-3740/paper-125.pdf.

Our study focuses on leveraging sensor data to enhance predictive models for ALS and MS progression, with the aim of assisting clinicians in making more informed therapeutic decisions. We developed machine learning approaches to predict ALSFRS-R scores in ALS patients and relapses in MS patients using wearable and environmental data. The results were promising, demonstrating improved prediction accuracy when sensor data were incorporated.

Participating in the iDPPChallenge provided a great opportunity to share our findings on how data science can enhance clinical decision-making in neurodegenerative diseases