About
The Insu-LINE Project
The Insu-LINE Project
Breaking the LINE between type 2 diabetes and cardiovascular diseases with AI support
Project Overview
The project aims to develop a digital solution based on algorithms for life-course cardiovascular risk assessment in patients with type 2 diabetes mellitus. The INsu-LINE system is built around an idea of integrated and proactive healthcare, resting on four fundamental pillars that work in synergy.
National Integrated Data Network
Everything starts with the creation of a national network of integrated data, capable to connect patients’ clinical information in a secure and uniform manner, guaranteeing maximum respect for privacy. This solid knowledge base feeds an intelligent prevention system: using advanced algorithms, the system can identify at-risk patients in advance, allowing doctors to intervene with personalised treatments before the clinical picture worsens.
Clinical Decision Support Dashboard
To facilitate this task, we have designed a support tool for clinicians: an intuitive digital dashboard that transforms complex data into clear information, making clinical decisions faster and more effective.
New Standards of Care
The ultimate goal is not only technological, but structural: we want to establish new standards of care through shared guidelines, so that this model of excellence becomes a common asset for the entire national healthcare system.
Interdisciplinary Collaboration
The true strength of INsu-LINE lies in its ability to make different worlds communicate. Managing a complex condition like diabetes requires a 360° vision, starting with close collaboration between specialist medicine (diabetologists and cardiologists) and epidemiology. Together, these experts identify critical risk factors and contextualise clinical data according to the different demographic and environmental profiles of patients.
From Clinical Knowledge to Technological Reality
This clinical knowledge is then translated into technological reality thanks to the work of experts in Artificial Intelligence and Data Science, who develop predictive models, and Biostatistics, which scientifically validates each result. Medical informatics and ethics and regulatory experts ensure that all of this takes place in a protected and excellent environment: their role is fundamental to ensuring the highest quality of data and full respect for patient rights and privacy regulations (GDPR). Only through this interweaving of expertise is it possible to transform large amounts of data into more human, safe and precise care.
General Objectives
The INsu-LINE project represents an important Italian research and innovation initiative aimed at transforming diabetes management through the integration of digital technologies, such as artificial intelligence, machine learning and big data. The central objective of INsu-LINE is to overcome traditional care models by creating an advanced digital platform capable of securely and uniformly connecting clinical information at national level. Through the use of dedicated algorithms, the system aims to preventively identify patients at risk of adverse cardiovascular events, offering doctors rapid decision support through intuitive digital dashboards.
This approach not only personalises treatment, but also aims to define new standards of care and shared strategies for the entire national healthcare system. The complex nature of this challenge requires deep interdisciplinary collaboration involving diabetologists and cardiologists, artificial intelligence experts, biostatisticians, medical informaticians and epidemiologists, as well as ethics and regulatory specialists to ensure full respect for privacy and patient rights. The focus on prevention is dictated by the very nature of diabetes mellitus, a chronic condition that drastically increases the risk of cardiovascular complications, the main cause of mortality in these patients.
Specific Objectives
Objective 1
BUILDING A FEDERATED AND NORMALISED KNOWLEDGE BASE
The first milestone of the project concerns the creation of a system capable of collecting and organising longitudinal patient data across the national territory. Leveraging interoperability and the semantic nature of data, INsu-LINE enables the harmonisation of information from different clinical structures. This process is essential for overcoming current fragmentation and creating a shared information heritage, fundamental for implementing effective prevention guidelines for both individual patients and the entire population.
Objective 2
DESIGN OF A NEW PATIENT STRATIFICATION ALGORITHM
The second objective aims to develop an advanced algorithm to predict the risk of clinical complications due to disease progression. Currently in Italy there is no population study on diabetes with a sample size comparable to international standards; INsu-LINE intends to fill this gap by analysing the medical records of at least 300,000 patients. This will make the project results the first nationally validated study for the effectiveness of life-course cardiovascular risk calculation in type 2 diabetes.
Objective 3
DEVELOPMENT OF A DISTRIBUTED DIGITAL SOLUTION
The third objective consists of translating research into practical tools. A flexible digital platform will be created, based on a micro-services architecture, which will allow analysis algorithms to be run and offer operators an intuitive “dashboard” for data exploration. The true innovation of this system is the ability to integrate risk indicators directly into electronic health records, acting as an accelerator for future clinical studies and new scientific investigations.
Objective 4
PRESENTATION OF RESULTS AND SYSTEM SUSTAINABILITY
The final objective is aimed at the dissemination of results and the continuity of the project over time. Specific guidelines will be released to describe the use of the platform and procedures to allow new entities to join the federated network. Through collaboration between academia, industry and healthcare, and with the support of the DARE Foundation, INsu-LINE aims to become a national and international reference point, raising stakeholders’ awareness of the importance of Artificial Intelligence as a daily tool for prevention and early diagnosis.
Expected Results
Personalisation of care.
The use of advanced algorithms and new risk indicators allows for early identification of diabetic patients most exposed to cardiovascular complications. This enables doctors to intervene preventively not only with specific therapies, but also by acting on lifestyle habits. Thanks to Artificial Intelligence, treatment becomes tailored: depending on the detected risk, it is possible to intensify monitoring, personalise medications or refer the patient to targeted specialist consultations.
Optimisation of healthcare resources.
INsu-LINE allows better management of healthcare system resources, reducing waste and costs for unnecessary interventions. Risk indicators help establish priorities, concentrating visits and treatments on high-risk patients. Timely intervention also reduces the need for hospitalisations and invasive procedures, easing the overall burden on public structures.
Improvement of prevention and management of complications.
The ability to accurately stratify patients makes it possible to prevent serious acute events, such as heart attacks or strokes, which represent the main causes of mortality in diabetes. The direct result is a reduction in mortality and a general improvement in clinical management, with a profound impact on the long-term health of citizens.
Decision support for physicians.
The project provides healthcare professionals with digital support based on advanced predictive models, which reduces variability in care and increases the accuracy of clinical choices. Constant integration with patient data enables continuous monitoring, giving doctors the ability to adapt therapies in real time based on the evolution of the clinical picture.
Strategic use of Big Data and Artificial Intelligence.
From a technological point of view, INsu-LINE marks a step forward in the integration of complex data (clinical, genetic and lifestyle-related). These algorithms are not static, but can evolve over time adapting to new information, ensuring ever-greater accuracy compared to traditional stratification models.
Improvement of patients’ quality of life.
Beyond clinical aspects, the project increases patients’ awareness of their health status. Knowing that they are included in an effective monitoring pathway reduces anxiety and stress related to uncertainty about disease progression. Greater knowledge also promotes the adoption of healthier habits and a more active and peaceful management of their condition.
Reduction of long-term costs.
Preventing complications and slowing the progression of the disease means drastically reducing future costs related to surgical interventions, intensive care and complex chronic treatments. This ensures the economic sustainability of the healthcare system, allowing better allocation of funds and greater efficiency in a context of limited resources.
Impact Acceleration Programme
The INsu-LINE Impact Acceleration Programme promotes collaborations with foundations, philanthropic organisations and strategic stakeholders to strengthen the project’s scientific, social and territorial impact. It supports dissemination, stakeholder engagement, sustainability and the development of the INsu-LINE Observatory.