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Rehabilitation Assessment of Motor Function In Cerebral Palsy Using Explainable AI

1. juni 2026 opdateret af: Riphah International University

Rehabilitation Assessment of Motor Function in Ambulatory Children With Cerebral Palsy Using Explainable Machine Learning

The goal of this observational study is to develop and validate an AI-based prediction model for functional mobility and gait outcomes in children with cerebral palsy using low-cost clinical and gait data collected in rehabilitation settings in Pakistan. The study aims to determine whether machine learning models can accurately predict mobility status, gait symmetry, and functional independence in ambulatory and non-ambulatory children with cerebral palsy.

The main questions it aims to answer are:

  • Can clinical and gait-related variables accurately predict functional mobility and gait outcomes in children with spastic cerebral palsy?
  • Can video-based assessment tools provide clinically useful data for AI-based rehabilitation assessment in low-resource settings?

Researchers will analyze clinical, functional, and gait data to identify patterns associated with mobility limitations and rehabilitation outcomes.

Participants will:

  • Undergo clinical and functional assessments, including measures of balance, mobility, posture, and functional independence.
  • Perform gait and movement tasks while data are collected using AI-based video analysis tools.
  • Participate in routine rehabilitation sessions while their movement and functional performance are recorded for analysis.
  • Provide demographic and clinical information relevant to cerebral palsy severity and functional status.

Studieoversigt

Status

Ikke rekrutterer endnu

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

Children with cerebral palsy (CP) commonly experience limitations in functional independence and mobility, which significantly affect participation and quality of life. Accurate assessment of these functional abilities is essential for rehabilitation planning, prognosis estimation, and monitoring treatment outcomes. However, conventional assessment methods largely depend on therapist observation and standardized clinical scales, which may be subjective, time-consuming, and less sensitive to complex interactions among clinical variables. In low-resource rehabilitation settings, the limited availability of advanced assessment technologies further restricts objective and data-driven clinical decision-making. Therefore, there is a growing need for innovative, accessible, and reliable approaches to improve rehabilitation assessment in children with CP.

The novelty of this study lies in the application of machine learning techniques to rehabilitation assessment of functional independence and mobility in children with cerebral palsy. Unlike traditional approaches that rely solely on isolated clinical interpretation, this study aims to integrate multiple clinical and functional parameters to identify predictive patterns associated with mobility and independence outcomes. The proposed approach introduces a data-driven and potentially more objective framework for rehabilitation assessment, supporting early identification of functional limitations and personalized intervention planning. Additionally, conducting this research in a low-resource context contributes further novelty by exploring the feasibility of implementing machine learning-based rehabilitation assessment tools in settings where advanced gait laboratories and expensive technologies are not readily available.

Undersøgelsestype

Observationel

Tilmelding (Anslået)

200

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

Studiesteder

  • Pakistan
      • Islamabad, Pakistan
      • Islamabad, Pakistan
        • Army special education Academy
      • Islamabad, Pakistan
      • Karachi, Pakistan

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Barn
  • Voksen

Tager imod sunde frivillige

Ingen

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

Children with cerebral palsy classified within Gross Motor Function Classification System (GMFCS) Levels I-III who are ambulatory with or without assistive devices and receiving routine physiotherapy rehabilitation. Participants from all cerebral palsy subtypes will be included for clinical, functional, and gait assessment related to AI-based evaluation of functional mobility and gait outcomes.

Beskrivelse

Inclusion Criteria:

  • Age 4 to18 years
  • Diagnosed any motor type of cerebral palsy (spastic, dyskinetic, ataxic, mixed),)
  • GMFCS levels I -III (able to walk with or without an assistive device).
  • All participants must be able to ambulate at least 10 meters with or without an assistive device.
  • Capable of following simple verbal instructions.
  • Parental informed consent and child assent

Exclusion Criteria:

  • Recent orthopedic or neurosurgical interventions (<6 months).
  • Uncontrolled seizures affecting gait.
  • Non-ambulatory (GMFCS IV-V) or cognitive impairments preventing cooperation.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
Intervention / Behandling
Ambulatory Children with Spastic Cerebral Palsy (GMFCS I-III)
Children diagnosed with spastic cerebral palsy who are ambulatory and classified within Gross Motor Function Classification System (GMFCS) Levels I to III. Participants will undergo clinical, functional, and gait assessments for AI-based prediction of functional mobility and gait outcomes
Andet: AI-Based Functional Mobility and Gait Assessment
Participants will continue receiving their standard/routine physiotherapy rehabilitation program as prescribed by their treating therapist. The study will involve observational collection of clinical, functional, and gait-related data using standardized assessment tools, and AI-based video analysis. No additional therapeutic intervention will be administered specifically for research purposes.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
GMFM-66
Tidsramme: Baseline to 6 months followup
GMFM (Gross Motor Function Measure) Reliability: Excellent. Internal consistency Cronbach's α ~0.997-1.00; intra- and inter-rater ICC ~0.994-0.999 (both GMFM-88 & GMFM-66) Validity: Construct and concurrent validity supported by strong correlations with related motor function classifications (e.g., GMFCS, PEDI mobility)
Baseline to 6 months followup
Markerless Gait Analysis
Tidsramme: Baseline to 6 months

Gait videos will be processed using a validated markerless pose estimation framework (MediaPipe) Spatiotemporal and kinematic gait parameters will be extracted, including but not limited to:

  • Step length symmetry
  • Cadence
  • Stride time variability
  • Joint angle trajectories
  • Temporal asymmetry indices
Baseline to 6 months
Edinburgh visual gait scale (EVGS)
Tidsramme: Baseline to 6 Months
Edinburgh visual gait scale (EVGS) EVGS can be a supportive tool that adds quantitative data instead of only qualitative assessment to a video only gait evaluation. Interobserver agreement is 60-90% and Kappa values are 0.18-0.85 for the 17 items in EVGS. Reliability is higher for distal segments (foot/ankle/knee 63-90%; trunk/pelvis/hip 60-76%). Agreement between EVGS and 3DGA is 52-73%.
Baseline to 6 Months
WeeFIM (Functional Independence Measure for Children)
Tidsramme: Baseline to 6 months
WeeFIM (Functional Independence Measure for Children) Reliability: High internal consistency and ICCs (motor and cognitive scales) ~0.91-0.98 in children with cerebral palsy Validity: Construct and external validity supported (scale fits Rasch model expectations and correlates with related developmental measures)
Baseline to 6 months

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
System usabiity scale (SUS)
Tidsramme: 6 months
10 items likert scale questionnaire evaluating percieved usability and acceptability
6 months

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Riphah International University

Efterforskere

  • Ledende efterforsker: Sidra Ghias, PhD* Rehab, Riphah International university Isalambad

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Anslået)

10. juni 2026

Primær færdiggørelse (Anslået)

30. juni 2027

Studieafslutning (Anslået)

30. december 2027

Datoer for studieregistrering

Først indsendt

18. maj 2026

Først indsendt, der opfyldte QC-kriterier

1. juni 2026

Først opslået (Faktiske)

5. juni 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

5. juni 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

1. juni 2026

Sidst verificeret

1. juni 2026

Mere information

Begreber relateret til denne undersøgelse

Nøgleord

Andre undersøgelses-id-numre

  • RCRAHS-ISB/REC/PhD/011111

Plan for individuelle deltagerdata (IPD)

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