用于儿童脑瘫的实时肌肉骨骼代理：一项可信度试点研究
Real-Time Musculoskeletal Surrogates for Pediatric Cerebral Palsy: a Credibility Pilot
- College of Science and Technology, North Carolina A & T State University（北卡罗来纳农工州立大学科学技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究开发了基于OpenSim的受试者条件因果神经代理，在9名儿童脑瘫步态数据集上验证其低延迟、高精度，建立了儿科肌肉骨骼代理的无泄漏评估框架，明确了临床数字孪生的后续核心挑战。
AI中文摘要：
实时肌肉骨骼（MSK）代理可支持儿童脑瘫（CP）的个性化康复，但其可信度取决于按受试者评估、低推理延迟和校准不确定性。我们开发了一种基于受试者条件的因果神经代理，使用OpenSim衍生的静态参数、关节运动学时间序列、真实肌肉容量以及仅训练用的扰动项。在包含9名儿童的真实儿童脑瘫步态数据集上，我们对6名开发受试者采用留一受试者交叉验证，对3名锁定测试受试者评估一次冻结配置。该代理能准确重现肌肉肌腱长度（开发验证中R平方=0.92，锁定受试者上约为0.95；归一化均方根误差nRMSE<8%），且仅需亚毫秒至数毫秒的神经推理，远低于交互式康复100毫秒的目标。相比之下，直接肌肉力估计在该小型异质规模上仍不稳定：合并指标可能高估受试者内、每肌肉的准确性。蒙特卡洛可信度试点进一步显示，仅传播±5%的人体测量学和肌肉容量变异会产生严重过度自信的名义90%区间（力覆盖率约4%，肌肉肌腱（MT）长度覆盖率低于1%）。这些结果建立了用于儿童MSK代理的无泄漏评估和可信度框架，同时确定力建模和认知不确定性是临床可信数字孪生的核心后续挑战。
英文摘要：
Real-time musculoskeletal (MSK) surrogates could support personalized rehabilitation for children with cerebral palsy (CP), but their credibility depends on subject-wise evaluation, low inference latency, and calibrated uncertainty. We develop a subject-conditioned causal neural surrogate using OpenSim-derived static parameters, temporal joint kinematics, true muscle capacities, and training-only perturbations. On a real pediatric CP gait dataset comprising nine children, we use leave-one-subject-out validation on six development subjects and evaluate a frozen configuration once on three locked test subjects. The surrogate accurately reproduces musculotendon lengths (R-square = 0.92 in development validation and approximately 0.95 on locked subjects; nRMSE < 8%) while requiring only sub-millisecond to few-millisecond neural inference, well below a 100 ms interactive-rehabilitation target. In contrast, direct muscle-force estimation remains unstable at this small, heterogeneous scale: pooled metrics can overstate within-subject, per-muscle accuracy. A Monte Carlo credibility pilot further shows that propagating only +/-5% anthropometry and muscle-capacity variation produces severely overconfident nominal 90% intervals (approximately 4% force coverage and below 1% MT-length coverage). These results establish a leakage-free evaluation and credibility framework for pediatric MSK surrogates, while identifying force modeling and epistemic uncertainty as the central next challenges for clinically credible digital twins.
