NCT06073522: AInCP

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

Not yet recruiting Last updated 10 October 2023

What this trial tests

trial testing Artificial Intelligence for combining multi-domain data acquisition in Unilateral Cerebral Palsy in 500 participants. Not yet recruiting.

Timeline

10 October 2023

Primary endpoint
30 October 2024

30 June 2027

Quick facts

Lead sponsor	IRCCS Fondazione Stella Maris
Status	Not yet recruiting
Study type	OBSERVATIONAL
Enrollment	500
Start date	10 October 2023
Primary completion	30 October 2024
Estimated completion	30 June 2027
Sites	2 locations across Italy, Spain

Drugs / interventions tested

• Artificial Intelligence for combining multi-domain data acquisition

Conditions studied

• Unilateral Cerebral Palsy — all drugs for Unilateral Cerebral Palsy →

Sponsor

IRCCS Fondazione Stella Maris — full company profile →

Who can join

Adults 5 to 15, any sex, with Unilateral Cerebral Palsy. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

2 peer-reviewed publications reference this trial (live from Europe PMC):

1. Relationship between participation across daily environments and bimanual performance in children with unilateral cerebral palsy: an ICF-based cross-sectional study.
   Coello-Villalón M, López-Muñoz P, Sgandurra G, Kalkantzi A, et al · · 2026 · PMID 41803796 · DOI 10.1186/s12887-026-06712-x
2. Relationship Between Participation Across Daily Environments and Bimanual Performance in Children With Unilateral Cerebral Palsy: An ICF-Based Cross-Sectional Study
   Coello-Villalón M, López-Muñoz P, Sgandurra G, Kalkantzi A, et al · · 2026 · DOI 10.21203/rs.3.rs-8872346/v1