Periodic Reporting for period 1 - 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)
Período documentado: 2022-06-01 hasta 2023-11-30
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.
During the initial 18 months of the AInCP Project, substantial progress has been achieved due to the fruitful collaboration among the Coordinator and all the Consortium partners; this collaboration has been pivotal, for the successful achievement of the goals.
The starting activities focused extensively on managing the Project and on laying the basis for dealing with the observational study in the best way. Also, the setting up of the server and cloud infrastructure has been put in place to collect the data in a secure way and in compliance with regulations. In parallel, operational activities were carried out primarily focused on the definition of the clinical and technological specifications for the clinical studies (i.e. observational and rehabilitative) and for the overall AInCP approach. The continuous brainstorming among different professionals resulted in the definition of the AInCP specifications needed for the development of the AInCP platform and set as the first Milestone. Clinical activities, conducted in close collaboration with parents (and following the input from all partners), were mainly dedicated to formulating the clinical protocol for the observational study. Great collaboration and effort have been devoted by the clinical staff to select the most efficient evaluative tools to be used in the observational study, achieving the second Milestone of the Project. In the meantime, the technological activities predominantly focused on designing and developing the key components of the AInCP platform, leveraging feedback from clinicians. Moreover, we started the activities on co-design of the Decision Support Tools (DSTs) and the development of the Machine Learning (ML) models. Moreover, Health Technology Assessment (HTA) and ethical activities played a crucial supportive role in framing the clinical and technological activities.
The starting activities focused extensively on managing the Project and on laying the basis for dealing with the observational study in the best way. Also, the setting up of the server and cloud infrastructure has been put in place to collect the data in a secure way and in compliance with regulations. In parallel, operational activities were carried out primarily focused on the definition of the clinical and technological specifications for the clinical studies (i.e. observational and rehabilitative) and for the overall AInCP approach. The continuous brainstorming among different professionals resulted in the definition of the AInCP specifications needed for the development of the AInCP platform and set as the first Milestone. Clinical activities, conducted in close collaboration with parents (and following the input from all partners), were mainly dedicated to formulating the clinical protocol for the observational study. Great collaboration and effort have been devoted by the clinical staff to select the most efficient evaluative tools to be used in the observational study, achieving the second Milestone of the Project. In the meantime, the technological activities predominantly focused on designing and developing the key components of the AInCP platform, leveraging feedback from clinicians. Moreover, we started the activities on co-design of the Decision Support Tools (DSTs) and the development of the Machine Learning (ML) models. Moreover, Health Technology Assessment (HTA) and ethical activities played a crucial supportive role in framing the clinical and technological activities.
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.