Testing AI to assess and home-train arm use in children with one-sided cerebral palsy
Studies AI tools to assess and guide home arm training in children with cerebral palsy.
Plain-language title generated from the registry record
Registry title: Validation of AI for Personalized Assessment and Rehabilitation of Upper Limb in Children With Unilateral Cerebral Palsy
Key facts
- Where
- Toledo, SpainSee the site
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 5 and 15 years old?
Source: NCT06073522, who can take part
Where in Spain
- Universidad de Castilla La ManchaToledo, SpainNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- IRCCS Fondazione Stella Maris
- Trial code
- NCT06073522
- Record updated
- 10 October 2023
- Original 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.Read full original descriptionShow less
Clinical Validation of Artificial INtelligence for Providing a Personalized Motor Clinical Profile Assessment and Rehabilitation of Upper Limb in Children With Unilateral Cerebral Palsy
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.
Inclusion: Children with confirmed diagnosis of UCP mainly spastic form; Ages from 5 to 15 years old; Manual ability levels from I to III in the Manual Ability Classification System (MACS) Exclusion criteria; Severe Upper Limb (UpL) impairment (MACS ≥ level IV: inability to grasp); Botulinum toxin-A injections in UpL within 6 months prior to study entry; UpL surgery within 12 months prior to study entry; Severe comobordities and/or severe cognitive disability; For Typically developing children:; Ages from 5 to 15 years old; No documented clinically relevant disorders.
For the group of children with UCP:.