用单视角视频实现儿童步态客观量化，助力脑瘫患者长期监测
Quantifying Rodda and Graham Gait Classification from 3D Markerless Kinematics derived from a Single-view Video in a Heterogeneous Pediatric Clinical Cohort
- 基于单视角视频开发无标记步态分析流程，直接估算膝踝关节异常评分
- 膝关节评分预测准确度达R²=0.80，踝关节达R²=0.57，可识别83%过度屈曲患儿
- 支持长期轨迹追踪，适合资源有限的临床环境持续评估治疗效果
脑瘫（CP）是儿童最常见的终身运动障碍。约75%的脑瘫患儿可行走，但其步行功能在成年中期有四分之一到一半会恶化。Rodda和Graham分类系统通过3D步态分析（3D-IGA）获取的膝踝矢状面z分数量化步态异常，但3D-IGA成本高且仅限于专业中心，而人工观察评估一致性中等。本研究开发了一种无标记步态分析流程，直接从单视角临床步态视频中估算膝踝z分数。在152名儿童（88名男性，63名女性；年龄12.1±4.0岁；60种主要诊断，其中54例为脑瘫）的1,058个双侧肢体样本中，矢状面模型对膝关节z分数的预测达到R²=0.80±0.02，CCC=0.89±0.02；踝关节为R²=0.57±0.02，CCC=0.72±0.02。二分类筛查过度膝屈曲的AUROC达0.88，正确识别83%的异常患儿；应用原始分类规则时，7类分类准确率为43±1%，宏平均AUROC=0.78±0.01，踝关节预测误差仍是主要瓶颈。该方法不仅可用于横断面筛查，还可实现跨访视的连续评分追踪，为疾病进展与治疗反应提供观测依据，超越传统观察量表。结果表明，视频驱动的z分数估算、异常筛查及纵向轨迹追踪具有可行性，有望推动低成本、客观化的步态评估普及。
原文摘要 · Abstract (English)
Cerebral Palsy (CP) is a neurological disorder of movement and the most common cause of lifelong physical disability in childhood. Approximately 75% of children with CP are ambulatory, and accurate gait assessment is central to preserving walking function, which deteriorates by mid-adulthood in a quarter to half of adults with CP. The Rodda and Graham classification system quantifies sagittal-plane gait deviations using ankle and knee z-scores derived from 3D Instrumented Gait Analysis (3D-IGA), but 3D-IGA is expensive and limited to specialized centers, while observational assessment shows only moderate inter-rater agreement. We developed a markerless gait analysis pipeline that quantifies Rodda and Graham knee and ankle z-scores directly from single-view clinical gait videos. Across 1,058 bilateral limb samples from 529 trials of 152 children (88 male, 63 female; age 12.1 $\pm$ 4.0 years; 60 distinct primary diagnoses, cerebral palsy the most common at $n=54$), the sagittal-view model achieved $R^2 = 0.80 \pm 0.02$ and CCC $= 0.89 \pm 0.02$ for knee z-scores and $R^2 = 0.57 \pm 0.02$ and CCC $= 0.72 \pm 0.02$ for ankle z-scores against 3D-IGA. Binary screening for excess knee flexion achieves AUROC $= 0.88$, correctly identifying 83% of affected children, and applying Rodda and Graham rules yields $43 \pm 1$% 7-class accuracy with macro-AUROC $= 0.78 \pm 0.01$, ankle prediction error remaining the primary bottleneck. Beyond cross-sectional screening, continuous z-scores support longitudinal trajectory tracking across visits, providing a quantitative substrate for monitoring disease progression and treatment response unavailable from observational scales. These results demonstrate the feasibility of video-based z-score estimation, excess-flexion screening, and longitudinal trajectory tracking as a path toward scalable, objective gait assessment in low-resource clinical settings.
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