Description du projet
Lutter numériquement contre l’arthrite psoriasique
Quelque 0,5 % de la population mondiale souffre d’arthrite psoriasique (AP), une maladie inflammatoire chronique et progressive. Environ un tiers des patients atteints de psoriasis développeront une AP. Les mécanismes à l’origine de cette transformation en AP sont inconnus, ce qui complique la pose d’un diagnostic précoce et le début du traitement approprié. La AP demeure une maladie chronique dont le traitement vise la rémission de la maladie. Or, la plupart des patients connaîtront une aggravation de la maladie. Le projet iPROLEPSIS, financé par l’UE, a pour ambition d’améliorer la détection précoce des symptômes de la AP et de réduire les retards de diagnostic. Il aura recours à une technologie d’IA avancée appliquée aux mégadonnées multisources afin de capturer et analyser de manière dynamique les données propres aux patients, ce qui permettra de garantir des soins davantage centrés sur le patient et d’apporter une contribution directe à l’amélioration de la qualité de vie des personnes souffrant d’AP.
Objectif
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.
Champ scientifique
Mots‑clés
Programme(s)
Régime de financement
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinateur
546 36 THESSALONIKI
Grèce