Engineering a Digital Twin for Diagnosis and Treatment of Multiple Sclerosis
Multiple sclerosis (MS) is a complex, chronic, and heterogeneous disease of the central nervous system that affects millions globally. The multifactorial nature of MS necessitates an adaptive and personalized approach to diagnosis, monitoring, and treatment. This paper proposes a novel Digital Twin for Multiple Sclerosis (DTMS) designed to integrate diverse data sources, including MRI, clinical biomarkers, and digital health metrics, into a unified predictive model. The DTMS aims to enhance the precision of MS management by providing real-time, individualized insights into disease progression and treatment efficacy. Through a federated learning approach, the DTMS leverages explainable AI to offer reliable and personalized therapeutic recommendations, ultimately striving to delay disability and improve patient outcomes. This comprehensive digital framework represents a significant advancement in the application of AI and digital twins in the field of neurology, promising a more tailored and effective management strategy for MS.
Tue 24 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
14:00 - 15:30 | Session 7: Digital Twins: AI & ARTechnical Track at HS 18 Chair(s): Bianca Wiesmayr LIT CPS Lab, Johannes Kepler University Linz | ||
14:00 20mPaper | AI Simulation by Digital Twins: Systematic Survey of the State of the Art and a Reference Framework Technical Track | ||
14:20 20mPaper | Engineering Interoperable Ecosystems of Digital Twins: A Web-based Approach Technical Track Andrea Giulianelli Alma Mater Studiorum - Università di Bologna, Samuele Burattini Alma Mater Studiorum - University of Bologna, Andrei Ciortea University of St. Gallen, Alessandro Ricci University of Bologna, Italy File Attached | ||
14:40 15mShort-paper | Towards Ontological Service-Driven Engineering of Digital TwinsEDTconf Best Short & Tool Paper Technical Track Bentley Oakes Polytechnique Montréal, Claudio Gomes Aarhus University, Denmark, Eduard Kamburjan University of Oslo, Giuseppe Abbiati Aarhus University, Elif Ecem Bas R&D Test Systems, Sebastian Engelsgaard LORC DOI Pre-print File Attached | ||
14:55 15mDemonstration | Practical design and implementation of an augmented reality based digital twin Technical Track Lionel Protin Luxembourg Institute of Sciences and Technology, Wassila Aggoune-Mtalaa Luxembourg Institute of Sciences and Technology, Carlos Kavka Luxembourg Institute of Sciences and Technology | ||
15:10 15mShort-paper | Engineering a Digital Twin for Diagnosis and Treatment of Multiple Sclerosis Technical Track Giordano d'Aloisio University of L'Aquila, Alessandro Di Matteo University of L'Aquila, Alessia Cipriani Sacro Cuore Catholic University of Rome, Daniele Lozzi University of L'Aquila, Enrico Mattei University of L'Aquila, Gennaro Zanfardino University of L'Aquila, Antinisca Di Marco University of L'Aquila, Giuseppe Placidi University of L'Aquila |