Validation of AI for Personalized Assessment and Rehabilitation of Upper Limb in Children With Unilateral Cerebral Palsy — AInCP  

Unilateral Cerebral Palsy Clinical Trial

Official title: Clinical Validation of Artificial INtelligence for Providing a Personalized Motor Clinical Profile Assessment and Rehabilitation of Upper Limb in Children With Unilateral Cerebral Palsy

Status Not yet recruiting

Clinical Trial Summary

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.

Clinical Trial Description

The project is configured as an international observational clinical non-profit study that aims to change the current management of care and intervention of children and adolescents with UCP, providing a new model of approach.

This study has international dimensions, in fact it involves 12 partners from 7 countries; children will be enrolled and tested by the four clinical centers, namely the IRCCS Stella Maris Foundation, the Universidad De Castilla - La Mancha (Spain), the Katholieke Universiteit of Leuven (Belgium) and the David Tvildiani Medical University (Georgia).

Enrollment and clinical and kinematic evaluations will be carried out by the centers involved in the study; as regards the analysis of all neuroimaging data, the Queensland Cerebral Palsy Research Centre of the University of Queensland (Australia) will be involved, as this center is an international expert on this.

Participation in the study will be voluntary and a signature of informed consent by the parent will be required.

For the group of children with UCP the inclusion criteria will be:

1. Confirmed diagnosis of CP with predominantly spastic hemiplegia

2. Mild to moderately severe impairment of upper limb function (MACS level I-III)

The exclusion criteria will be:

1. Severe impairment of the upper limbs (MACS level IV or V)

2. Botulinum toxin injection in the upper limb within 6 months prior to study entry

3. Upper limb surgery in the 6 months prior to study entry

4. Presence of severe comorbidities and/or severe intellectual disability For the sample of children with typical development, they will be defined with the exclusion criteria of clinically documented disorders.

Recruitment will take place only after the signature of the informed consent. Upon recruitment, some personal data will be acquired and a special database will be created built on the REDCap platform and processed anonymously.

The data will come from the following areas:

1. Multiaxial clinical assessments and questionnaires

Before the start of the evaluation, a screening of the clinical measures that are part of the protocol will be carried out, in order to report any recent administrations of the same tests, in order to, if necessary, choose a better time window in which to start participation in this study. The protocol is in fact composed of standardized tests and questionnaires usually used in clinical practice. The clinical evaluation, which will be organized in two consecutive days and will be divided into different tests, some directed to the child and others, such as questionnaires, carried out by / with the parents, included in the following areas:

• Clinical motor evaluation (strength, spasticity and mirror movements) and sensitivity;

• Evaluation of the functionality of the upper limbs with structured and semi-structured tests

• Cognitive and neuropsychological evaluation

• Classification systems and questionnaires for upper limb skills, independence in everyday life and aspects related to participation and quality of life

For the children with TD, only a very small part of the evaluations provided for children with UCP will be carried out, that is:

• Evaluation of upper limb function with a semi-structured assessment session

• Questionnaires for coordination skills, to define laterality and to explore aspects related to behavior

Finally, a survey will be proposed consisting of questionnaires on sleep quality, quality of life and familiarity and ability with technologies for all children (both those with UCP and those with TD) their parents and also health professionals.

2. Neuroimaging Structural Magnetic Resonance (MRI) images of the brain of children with UCP, commonly acquired for clinical purposes, will be collected and evaluated according to the semiquantitative evaluation scale, developed by the Stella Maris Foundation (FSM), in order to quantify the severity of brain injury. For children and/or adolescents who have already performed MRI, parental consent will be requested to access this data. Image evaluation will be carried out by anonymizing and transferring the images to the University of Queensland/CSIRO in Australia and then analysed by expert evaluators using automated software, producing quantitative measurements.

3. Analysis of upper limb movement during clinical evaluation and daily life All children and/or adolescents enrolled will be asked to wear sensors on both wrists, during clinical evaluations and for 2 weeks in order to investigate upper limb activity also during daily life. During this period, parents will have to fill out a diary reporting the main daily activities (for example the time of wake-up, falling asleep, lunch and dinner, any sporting activity, etc.). Questionnaires will also be administered to evaluate the feasibility and acceptability of the sensors by children and/or adolescents and their parents, with a short daily form and a more extensive form at the end of the period of recording. At the end of the 2 weeks, parents will be contacted to pick up the sensors directly from their home.

To protect privacy and anonymity, each subject will be assigned a numerical code that will be kept separately. In this way the database will not contain any identification data of the subject. Access to such data will be limited only to personnel directly involved in the study and all data will be processed anonymously.

Once all the data coming from the three domains (clinical assessment, neuroimaging and kinematic analysis), together with the questionnaires, will be collected, they will be inserted in a dedicated RedCap database.

Investigators of UNIPI will use AI algorithms to process and analyze all data collected at the Green Data Center: clinical outcome measures, trackers' data, results of the questionnaires and the outcome from neuroimaging analysis. The results of analysis will be packaged into the diagnostic Decision Support Tool (dDST), a software tool that will support clinicians in managing functional diagnosis. This tool will be accessed by the clinicians through a specifically designed dashboard. The purpose of the AI analysis is to understand how these data are correlated with the different clinical features and treatment responses that distinguish one individual from another. The clinician can be supported both in diagnostic and rehabilitative processes, positively impacting both the clinical decision-making and the subject healthcare. ;

Related Conditions & MeSH terms

• Cerebral Palsy
• Paralysis
• Unilateral Cerebral Palsy

• NCT number: NCT06073522
• Study type: Observational
• Source: IRCCS Fondazione Stella Maris
• Contact: Giuseppina Sgandurra, PhD, MD
• Phone: 3392472874
• Email: g.sgandurra@fsm.unipi.it
• Status: Not yet recruiting
• Start date: October 10, 2023
• Completion date: June 30, 2027