Periodic Reporting for period 2 - AInCP (clinical validation of Artificial Intelligence for providing a personalized motor clinical profile assessment and rehabilitation of upper limb in children with unilateral Cerebral Palsy)
Période du rapport: 2023-12-01 au 2025-05-31
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.
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.
The AInCP Project is making substantial progress thanks to the fruitful collaboration between the Coordinator and all Consortium partners. This collaborative approach has been pivotal to the successful achievement of the project’s goals.
Project activities have extensively focused on management and on establishing a solid foundation for conducting the observational and rehabilitative studies in the most effective manner. The server and cloud infrastructure for data collection and tele-rehabilitation has been set up to ensure secure data management in full compliance with applicable regulations.
A continuous process of brainstorming and co-design among professionals from different disciplines led to the development of the AInCP platform. Clinical activities, carried out in close collaboration with families and informed by partner input, primarily addressed the implementation of the observational study and the finalization of the clinical protocol for the rehabilitative study. Significant effort and teamwork from the clinical staff resulted in the creation of a core set of AOT (Action Observation Therapy) exercises and the related narrative context.
In parallel, technological efforts concentrated on the development of the AInCP platform, integrating feedback from clinicians. Additional activities included the co-design of Decision Support Tools (DSTs) and the development of Machine Learning (ML) models, leading to the identification of a digital biomarker for daily upper limb movement.
Finally, Health Technology Assessment (HTA) and ethical activities provided essential support, ensuring that both clinical and technological developments were appropriately framed and aligned with ethical standards.
Project activities have extensively focused on management and on establishing a solid foundation for conducting the observational and rehabilitative studies in the most effective manner. The server and cloud infrastructure for data collection and tele-rehabilitation has been set up to ensure secure data management in full compliance with applicable regulations.
A continuous process of brainstorming and co-design among professionals from different disciplines led to the development of the AInCP platform. Clinical activities, carried out in close collaboration with families and informed by partner input, primarily addressed the implementation of the observational study and the finalization of the clinical protocol for the rehabilitative study. Significant effort and teamwork from the clinical staff resulted in the creation of a core set of AOT (Action Observation Therapy) exercises and the related narrative context.
In parallel, technological efforts concentrated on the development of the AInCP platform, integrating feedback from clinicians. Additional activities included the co-design of Decision Support Tools (DSTs) and the development of Machine Learning (ML) models, leading to the identification of a digital biomarker for daily upper limb movement.
Finally, Health Technology Assessment (HTA) and ethical activities provided essential support, ensuring that both clinical and technological developments were appropriately framed and aligned with ethical standards.
This AInCP is addressing the barriers to the lack of a personalized medicine (PM) in child neurology. It represents a unique cooperative network among researchers from the fields of child neurology, rehabilitation, economics, management, statistics, engineering and medical devices industry guided by consumer involvement. It will be a highly innovative procedure to create specific tools that will change the current clinical diagnosis of the various clinical phenotypes of children with UCP and their monitoring, follow-up, planning and delivery of rehabilitation. The application of the highest scientific (e.g. neuroimaging, high-density EEG, hdEEG) and technological (wearable sensors, flexible sensors, ICT infrastructures, machine learning and deep-learning) approaches will provide progress beyond the state of the art.