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iPROLEPSIS

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iPROLEPSIS PSORIATIC ARTHRITIS INFLAMMATION EXPLAINED THROUGH MULTI SOURCE DATA...

iPROLEPSIS PSORIATIC ARTHRITIS INFLAMMATION EXPLAINED THROUGH MULTI SOURCE DATA ANALYSIS GUIDING A NOVEL PERSONALISED DIGITAL CARE ECOSYSTEM Psoriatic Arthritis (PsA) is a chronic, progressive, inflammatory disease affecting 1-2% of the general population, while manifesting in up to 30% of people with psoriasis (PsO). The transition from health to PsA is currently untr... see more

31/12/2026

unknown leader

6M€

Project Budget: 6M€

Project leader unknown leader

PARTICIPATION DEPralty Sin fecha límite de participación.

Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2022-12-01

HORIZON-HLTH-2022-STAYHLTH-02-01: Personalised blueprint of chronic i... Expected Outcome:This topic aims at supporting activities that are enabling or contributing to one o...

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Project duration: 48 months Date Start: 2022-12-01
End date: 2026-12-31

Project leader unknown leader

Project Budget 6M€

participation deadline Sin fecha límite de participación.

Project description Psoriatic Arthritis (PsA) is a chronic, progressive, inflammatory disease affecting 1-2% of the general population, while manifesting in up to 30% of people with psoriasis (PsO). The transition from health to PsA is currently untraceable; diagnosis of early PsA is challenging even in PsO patients. Untimely diagnosis is common and contributes to early deterioration of quality of life, also increasing the burden of the multiple comorbidities associated with PsA. In this vein, iPROLEPSIS aspires to shed light upon the health-to-PsA transition with a comprehensive multiscale/multifactorial PsA model employing novel trustworthy AI-based analysis of multisource and heterogenous (i.a., in-depth health, environmental, genetic, behavioural) data, digital phenotyping of inflammatory symptoms with emphasis on tracking of motor manifestations using smart devices and wearables, novel optoacoustic imaging-based markers of PsA in the skin and joints, and investigation of the role of mast cells in the PsA transition, to identify key drivers of the disease and support personalized models for PsA risk/progression prediction and monitoring as well as associated inflammation detection and severity assessment. To ultimately advance PsA diagnosis and care, the models will be translated into a digital health ecosystem comprising dependable tools for supporting healthcare professionals in disease screening, monitoring and treatment via quantitative, explainable evidence, and empowering people with/at risk of PsA with tailored insights and preventive interventions based on actionable factors for educated health management. The project will steer its research and development efforts following a trustworthy framework for ethical, lawful, and robust AI, and a user-centered co-creation approach based on constant involvement of key stakeholders during the design, development, and testing of the digital health ecosystem, securing successful integration of the latter in the continuum of care.

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