Work packages

The 6 Work Packages of the project will bring together various and complementary expertise from consortium partners.

Work Package 1

Management and coordination

WP1 is dedicated to project management and coordination, aiming to enable a smooth project workflow through efficient management structures, processes and tools.

Main objectives:

ensure optimal contractual, administrative, financial, scientific, technical, IP and innovation management;
assure project quality and results procedures, and effectively manage risks, safeguard ethics, regulatory compliance and proper data management.

Work Package 2

Foundation, data curation and co-creation

WP2 aims to create an up-to-date, foundational body of knowledge concerning PD risk, progression and medication response factors/drivers and relevant tracking technologies.

Main objectives:

source, harmonise and curate datasets required for the R&D activities of the project;
identify user needs and effectively engage with key stakeholders to steer research questions and co-create the AI-PROGNOSIS toolkit for PD;
establish a procedural framework for ensuring adherence of AI components to trustworthy and responsible AI principles.

Work Package 3

Predicting PD risk, progression and medication response

Based on smartphone / watch data, WP3 will develop novel digital biomarkers (dBMs) for tracking REM behavior disorders (RBD) and daytime somnolence and implement a set of existing dBMs, tracking motor and cognitive function in daily living.

Main objectives:

perform genetic profiling to discover/confirm factors of PD risk/progression/medication response from diverse genotypic data;
based on multi-source health and genetic data, develop trustworthy AI models for personalised prediction of PD risk, progression and medication response and discover/confirm relevant drivers/factors in the process.

Work Package 4

The AI-PROGNOSIS digital health ecosystem

WP4 will technically specify the AI-PROGNOSIS ecosystem and set up a robust data management infrastructure and DevOps / MLOps platforms.

Main objectives:

through an agile approach, develop the minimum viable products (MVPs) encompassing the dBMs and AI models for PD risk prediction and prognosis:

1) the mAI-Health app for persons without PD to track their personalised disease risk;

2) the mAI-Care app for PwP to track disease progression and medication efficacy;

3) the mAI-Insights Web app for HCPs with PD screening support, patients’ follow-up, and pharmacological treatment decision support (AIMED component).

Work Package 5

Clinical studies

WP5 will develop the study protocols and carry out the process for their timely approval by concerned bodies.

Main objectives:

set up a system for coordinating recruitment and efficiently managing clinical data/reporting;
conduct a series of multicentre clinical studies to (i) develop, validate and verify digital biomarkers for tracking key PD risk and progression factors, and (ii) externally validate, via proof of concenpt (PoC), the PD risk, progression and medication response predictive models and evaluate the acceptance of the associated AI-PROGNOSIS tools;
define the criteria and clinical utility evaluation plan of the AI-PROGNOSIS digital health ecosystem.

Work Package 6

Dissemination, communication and exploitation

WP6 will maximise visibility of the project and its outcomes, and facilitate knowledge exchange, through planning, implementation and monitoring of dissemination, communication, and networking activities.

Main objectives:

develop PD-related educational content;
set out a roadmap for regulatory approval of the AI-PROGNOSIS digital tools;
perform a thorough socio-economic/market analysis and develop concrete joint/individual exploitation plans.

Our AI-enabled tools

Work packages

Work Package 1

Management and coordination

Main objectives:

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