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 250 and 180 children with UCP, respectively, enrolled in Italy, Spain, Belgium and Georgia. 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, multidimensional 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.
The impact of the AInCP will cover a wide range of stakeholders: not only the concerned children with UCP and their parents, but also the clinical scientists, industrial and public arenas, policy makers, politicians, public authorities, the media and citizen groups, to ensure that evidence of AInCP service can be translated into policy and best practice in clinical research centers, rehabilitation centers/rehabilitation technology sector and CP reference centers. For these reasons AInCP will have a high impact from scientific, tecnological, economical and societal point of view. For scientific impact we expect to increase the use of AI approaches in childhood care and neuro-rehabilitation. For Economic and Tecnological impact we aim to increase the use of AI-based approaches allowing an improvement of accuracy in decision making processes and a reduction of the costs. Finally, from a societal point of view, we expect to increase the awareness on the CP and reduce the mental health burden in children and their relatives. Across all the impacts, we expect that AInCP will be an unique example for increasing projects based on co-design multidisciplinary approach.
