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clinical validation of Artificial Intelligence for providing a personalized motor clinical profile assessment and rehabilitation of upper limb in children with unilateral Cerebral Palsy

Project description

AI-assisted diagnostic and rehabilitative tools for personalised support of children with cerebral palsy

Unilateral cerebral palsy (UCP) is the most common chronic neurological condition. The EU-funded AInCP project aims to develop AI-assisted clinical decision support tools (DST) capable of providing personalised functional diagnosis, upper-limb (UpL) assessment and home-based interventions for children with UCP. The project will establish a clinical diagnosis and prognosis methodology for personalised UCP treatment using a multimodal approach comprising clinical phenotyping, advanced brain imaging and real-time monitoring of UpL function. At the same time, it will develop a personalised home-based treatment strategy based on advanced information and AI technologies. The AInCP goal is to validate personalised diagnostic and rehabilitative DST via extensive observational and rehabilitation studies.

Objective

Unilateral Cerebral palsy (UCP) is the most common neurological chronic disease in childhood with a significant burden on children, their families and health care system.
AInCP aims to develop evidence-based clinical Decision Support Tools (DST) for personalized functional diagnosis, Upper Limb (UpL) assessment and home-based intervention for children with UCP, by developing, testing and validating trustworthy Artificial Intelligence (AI) and cost-effective strategies. The AInCP approach will: i) establish a clinical diagnosis and accurate prognosis for treatment response of individual UCP profiles, by employing a multimodal approach including clinical phenotyping, advanced brain imaging and real-life monitoring of UpL function, and ii) provide personalized home-based treatment, from advanced ICT and AI technologies.
The AInCP will build upon personalized diagnostic and rehabilitative DST (dDST and rDST) to be developed and validated through large observational and rehabilitation studies, including at least 200 and 150 children with UCP, respectively. Using data driven and AI approach, dDST and rDST will be combined for developing a theranostic DST (tDST) that will allow the re-designing of an economical, ethical, sustainable decision-making process for delivering a personalized and validated approach, focused on the care, monitoring and rehabilitation of UpL in children with UCP. AInCP is a significant example of a transdisciplinary approach, where all project collaborators (clinicians, data scientists, physicists, engineers, economists, ethicists, SMEs, children and parent associations) will work closely together in building the AInCP approach. This approach will, therefore, hinge on transdisciplinary contributions, multi-dimensional data, sets of innovative devices and fair AI-based algorithms, clinically effective and able to reduce users? and market barriers of acceptability, reimbursability and adoption of the proposed solution.

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Keywords

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Programme(s)

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Topic(s)

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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-2021-DISEASE-04

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Coordinator

UNIVERSITA DI PISA
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.

€ 1 444 750,00
Address
LUNGARNO PACINOTTI 43/44
56126 PISA
Italy

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Region
Centro (IT) Toscana Pisa
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.

€ 1 444 750,00

Participants (10)

Partners (1)

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