View clinical trials related to Unilateral Cerebral Palsy.
Filter by:The purpose of the study is to compare the effect between mirror therapy and task-oriented training on hand function 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.
Paediatric (including perinatal) stroke has an incidence of between 1.3 and 13.0 per 100.000 yearly in Europe. 1/3 of children with neonatal and 50% of post-natal stroke will develop a hemiplegia with upper limb being generally more affected than lower limb and a severe impact on children's participation and quality of life. Opportunities to advance scientific knowledge of the influence of genomic variation on the pattern, presentations and prognosis of paediatric stroke are lacking. Conversely, the discovery could have an enormous potential to drive the rehabilitation that is the major component of the stroke patient's care and to achieve a good functional outcome. The present proposal aims to change the current management of care and intervention of children with hemiplegia due to stroke, by identifying relevant biomarkers coming from four different areas (omics, clinical assessment, neuroimaging, Information and Communication Technologies) in order to stratify the children and to create a novel transdisciplinary patient-centred model to optimize and tailor the rehabilitation treatment. As a diagnostic tool, the new workflow allows the set-up for planning an individualized treatment based on patient specific needs, creating a model for an evidence-based clinical decision-making process that starts from the measurements of specific biomarkers, clinical measurements and effective use of patient's Upper Limb. The feasibility of the planned approach can be applied for deeply analysing and understanding results of previous researches and in new pilot studies on already available rehabilitative treatments. The results will provide an example of how different kinds of integrated assessments can contribute to create a plan for the management of children with hemiplegia due to stroke, thus leading to a better understanding of the correlation between genetic and phenotypic data. Finally, the Health Technology Assessment will provide estimates of its national and regional cost effectiveness.