Validation of AI for Personalized Assessment and Rehabilitation of Upper Limb in Children With Unilateral Cerebral Palsy

Summary

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.

Conditions

• Unilateral Cerebral Palsy

Interventions

OTHER

Artificial Intelligence for combining multi-domain data acquisition

Artificial Intelligence and machine learning techniques to combine data coming from multidomains data collection (such as clinical multiaxial assessments and questionnaires, Neuroimaging, Upper limb movement analysis during clinical assessment and daily life )

Sponsors & Collaborators

• University of Pisa

  collaborator OTHER

• University of Castilla-La Mancha

  collaborator OTHER

• Fight The Stroke

  collaborator UNKNOWN

• SCUOLA SUPERIORE DI STUDI UNIVERSITARI E DI PERFEZIONAMENTO S ANNA

  collaborator OTHER

• Noldus Information Technology Bv

  collaborator UNKNOWN

• Khymeia

  collaborator UNKNOWN

• Tyromotion GMBH

  collaborator UNKNOWN

• The University of Queensland

  collaborator OTHER

• University of Salento

  collaborator OTHER

• IRCCS Fondazione Stella Maris

  lead OTHER

Principal Investigators

• Giuseppina Sgandurra, Md, PhD · IRCCS Fondazione Stella Maris

Eligibility

Min Age

5 Years

Max Age

15 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2023-10-10

Primary Completion

2024-10-30

Completion

2027-06-30

Countries

• Italy
• Spain

Study Locations