Psoriatic Arthritis Inflammation Explained Through Multi-Source Data Analysis Guiding a Novel Personalised Digital Care Ecosystem

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.

iPROLEPSIS 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.