桥接疾病课程迁移实现罕见神经系统疾病的智能手机平衡评估

文献信息

中文摘要

基于视频的运动评估有望实现可扩展的神经功能评估，但罕见病应用受到极端数据稀缺和小队列泛化能力差的限制。我们在NF2相关神经鞘瘤病（NF2-SWN）中开发并评估了一种基于智能手机的平衡评估框架，这是一种超罕见疾病，客观平衡评估具有临床价值但后勤上具有挑战性。其核心是一种分阶段的桥接疾病课程，通过中间帕金森病领域将基于骨骼的时空图模型从大规模动作识别适应到目标NF2-SWN队列（N=19），在防泄漏、受试者独立评估下将一个大领域偏移分解为两个更可学习的转换。三个临床动机模块——双侧不对称门控、时间相位注意和解剖组显著性——产生可解释的、面向临床医生的输出，与平衡评估一致。相对于直接迁移，桥接疾病迁移将Mini-BESTest总分的患者水平预测误差降低了40%，缩小了与评分者间一致性的差距，并产生了良好校准的跌倒风险分层。稳健性分析量化了对模拟采集变异性的敏感性，支持实用的智能手机记录方案。总之，这些结果支持在极端数据稀缺下NF2-SWN中隐私保护视频平衡评估的可行性。

英文摘要

Video-based motor assessment could enable scalable assessment of neurological function, but rare-disease applications are constrained by extreme data scarcity and poor generalization from small cohorts. We develop and evaluate a smartphone-based balance-assessment framework in NF2-related schwannomatosis (NF2-SWN), an ultra-rare disorder for which objective balance assessment is clinically valuable yet logistically challenging. At its core, a staged bridge-disease curriculum adapts a skeleton-based spatiotemporal graph model from large-scale action recognition through an intermediate Parkinson's disease domain to the target NF2-SWN cohort ( ${N}={19}$ ), decomposing one large domain shift into two more learnable transitions under leakage-proof, subject-independent evaluation. Three clinically motivated modules, bilateral asymmetry gating, temporal phase attention, and anatomical group saliency, yield interpretable, clinician-facing outputs aligned with balance assessment. Relative to direct transfer, bridge-disease transfer reduces patient-level prediction error by 40% on the Mini-BESTest total score, narrows the gap toward inter-rater agreement, and yields well-calibrated fall-risk stratification. A robustness analysis quantifies sensitivity to simulated acquisition variability, supporting a pragmatic smartphone recording protocol. Together, these results support the feasibility of privacy-preserving video-based balance assessment in NF2-SWN under extreme data scarcity.