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Artificial intelligence-based Parkinson’s disease risk assessment and prognosis

Project description

AI advances Parkinson’s disease diagnosis

Parkinson's disease (PD) is characterised by the progressive loss of dopamine-producing brain cells, leading to diverse motor and non-motor symptoms. PD is often missed or misdiagnosed as early signs are subtle and common with other diseases, while selecting the optimal treatment is usually a lengthy process, leading to unnecessary suffering. Funded by the Horizon Europe programme, the AI-PROGNOSIS project aims to advance PD diagnosis and care by harnessing the power of AI. The consortium will develop predictive models for PD risk and prognosis based on in-depth health and genetic data and combine them with specific biomarkers measured in everyday life. Project work will lead to the creation of a digital health toolkit offering persons affected by PD and healthcare professionals valuable insights for informed health management.

Objective

Parkinson’s disease (PD) is the most common neurodegenerative movement disorder, with a multifactorial aetiology, heterogeneous manifestation of motor and non-motor symptoms, and no cure. PD is often missed or misdiagnosed, as early symptoms are subtle and common with other diseases, allowing for considerable damage to occur before treatment. Moreover, selecting the optimal medication regimen is usually a lengthy, “trial and error” process, leading to critical, costly non-adherence. Following a trustworthy and inclusive approach to AI development and based on multidisciplinary expertise and broad stakeholder engagement, AI-PROGNOSIS aims to advance PD diagnosis and care by: 1) developing novel, predictive AI models for personalised PD risk assessment and prognosis (in terms of time to higher disability transition and response to medication) based on multi-source patient records and databases, including in-depth health, phenotypic and genetic data, 2) implementing a system of biomarkers informing the AI models by tracking key risk/progression markers in daily living, and ultimately 3) translating the models and digital biomarkers into a validated, privacy-aware AI-driven toolkit, supporting healthcare professionals (HCPs) in disease screening, monitoring and treatment optimization via quantitative, explainable evidence, and empowering individuals with/without PD with tailored insights for informed health management.

Fields of science (EuroSciVoc)

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

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(opens in new window) HORIZON-HLTH-2022-STAYHLTH-01-two-stage

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Coordinator

ARISTOTELIO PANEPISTIMIO THESSALONIKIS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 555 000,00
Address
KEDEA BUILDING, TRITIS SEPTEMVRIOU, ARISTOTLE UNIVERSITY CAMPUS
546 36 THESSALONIKI
Greece

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Region
Βόρεια Ελλάδα Κεντρική Μακεδονία Θεσσαλονίκη
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 555 000,00

Participants (15)

Partners (2)

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