Rehabilitation Assessment of Motor Function in Ambulatory Children With Cerebral Palsy Using Explainable Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 200
- 试验地点
- 4
- 主要终点
- GMFM-88
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 4 Years 至 18 Years（Child, Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •Recent orthopedic or neurosurgical interventions (<6 months).
- •Uncontrolled seizures affecting gait.
- •Non-ambulatory (GMFCS IV-V) or cognitive impairments preventing cooperation.
研究组 & 干预措施
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
干预措施: AI-Based Functional Mobility and Gait Assessment (Other)
结局指标
主要结局
GMFM-88
时间窗: 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)
Markerless Gait Analysis
时间窗: Baseline to 6 months
Gait videos will be processed using a validated markerless pose estimation framework . 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
Edinburgh visual gait scale (EVGS)
时间窗: 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%.
WeeFIM (Functional Independence Measure for Children)
时间窗: 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)
次要结局
- Feasibility of markerless video-based gait analysis for routine physiotherapy assessment in low-resource clinical settings(Through study completion, 12 month)
- Predictive accuracy of the machine learning model for gait and motor function(At model validation ,post data collection ,6 month)
- Robustness of model predictive performance and change in functional and gait outcomes across heterogeneous therapy exposure contexts(Baseline to 6-month follow-up)