About
The Insu-LINE Project
The Insu-LINE Project
AI-driven prevention for cardiovascular risk in type 2 diabetes
INSU-LINE is a research project funded under Italy’s National Recovery and Resilience Plan (PNRR) within the DARE – Digital Lifelong Prevention programme and coordinated by the University of Naples Federico II.
The project focuses on improving the prevention of cardiovascular complications in people living with type 2 diabetes. By integrating health data from multiple clinical centres and applying advanced artificial intelligence models, INSU-LINE aims to support earlier identification of cardiovascular risk and enable more personalised and preventive care strategies.
Through collaboration between clinicians, researchers, and digital health experts, the project develops interoperable data infrastructures and predictive models that help healthcare professionals make more informed decisions. By anticipating risk earlier, INSU-LINE contributes to improving long-term outcomes for patients and supporting more sustainable healthcare systems.
A data-driven approach to cardiovascular risk prevention
Data integration across clinical centres
INSU-LINE integrates longitudinal health and care data from multiple clinical centres across Italy. By harmonising heterogeneous datasets and ensuring interoperability, the project creates a robust knowledge base to support advanced analytics and risk prediction models.
AI-based cardiovascular risk prediction
Using artificial intelligence and advanced predictive modelling, INSU-LINE develops tools to identify cardiovascular risk earlier in people living with type 2 diabetes. These models provide actionable insights to healthcare professionals, enabling more personalised and preventive care pathways.
Key objectives of the INSU-LINE project
Improve cardiovascular risk stratification
Develop AI-driven models to detect cardiovascular risk earlier in people living with type 2 diabetes.
Integrate clinical and health data
Combine longitudinal health and care data from multiple clinical centres to create interoperable data infrastructures.
Enable personalised prevention strategies
Support healthcare professionals with predictive tools that allow earlier and more targeted interventions.