Project R-15958

DT4CARE : Harnessing Digital Twins for Personalized Healthcare Solutions (Research)

This project aims to develop a patient-specific digital twin (DT) model to enhance rehabilitation outcomes by leveraging remote therapeutic monitoring (RTM) data and AI-driven predictive analytics. Focusing initially on knee arthroplasty recovery, the research seeks to optimize treatment parameters, improve decision-making for healthcare providers (HCPs), and reduce intervention costs. Machine learning (ML) and simulation techniques will be used to construct dynamic DTs, targeting prediction accuracy and selecting optimization methods for treatment adjustments. DT performance will be validated against real-world outcomes, ensuring clinical usability through iterative prototyping and stakeholder feedback. The innovation lies in bridging gaps in personalized rehabilitation, combining RTM data with predictive modeling and simulation-based optimization. By enabling HCPs to test treatment scenarios in a virtual environment, the DT model aims to accelerate recovery, improve care quality, and reduce costs.

01 January 2025 - 31 December 2028