帕金森病是一种常见的神经退行性疾病, 会逐步破坏患者运动功能和部分认知功能, 且发病隐匿、不可治愈, 为患者及家人带来沉重负担. 然而, 帕金森病的临床诊断通常依赖主观评估量表, 会同时受到评估者主观性、被评估者回忆偏差的影响. 目前, 有大量研究从各个模态探索了帕金森病的生理特征, 并借此提供了客观量化辅助诊断方法. 但是, 神经退行性疾病种类繁多、影响类似, 从帕金森病表征出发的单模态方法特异性问题仍有待解决. 为此, 搭建一套包含帕金森病异常诱发范式的多模态辅助诊断系统. 首先, 根据正态分布检验结果进行特征的参数检验, 构建具有统计学意义的特征集(p<0.05); 其次, 在临床环境中收集38例带有MDS-UPDRS评分量表的多模态数据; 最后, 基于步态和眼动模态, 分析不同特征组合方式评估帕金森病的显著性; 验证虚拟现实场景下高沉浸诱发型任务范式和多模态帕金森病辅助诊断系统的有效性; 其中步态与眼动模态综合使用, 只需要进行2–4个任务, 平均AUC和平均准确率就分别能达到0.97和0.92.

Parkinson’s disease is a widespread neurodegenerative disease that slowly impairs the motor and certain cognitive functions of patients. It is insidious and incurable and can cause a significant burden on sufferers and their families. However, clinical diagnosis of Parkinson’s disease typically relies on subjective rating scales, which can be influenced by the examinee’s recall bias and assessor subjectivity. Numerous researchers have investigated the physiological aspects of Parkinson’s disease from multiple modalities and have provided objective and quantifiable tools for auxiliary diagnosis. However, given the diversity of neurodegenerative diseases and the similarities in their effects, it remains a problem among unimodal methods built upon the representations of Parkinson’s disease to identify the disease uniquely. To address this issue, a multimodal auxiliary diagnosis system comprising the paradigms that evoke aberrant behaviors of Parkinson’s disease is developed in this study. First, parametric tests of the features are performed based on the results of the normal distribution test, and statistically significant feature sets are constructed (p<0.05). Second, multimodal data are collected from 38 cases in a clinical setting using the MDS-UPDRS scale. Finally, the significance of different feature combinations for the assessment of Parkinson’s disease is analyzed based on gait and eye movement modalities; the high immersion triggered task paradigm and the multimodal Parkinson’s disease auxiliary diagnosis system are validated in virtual reality scenarios. It is worth noting that it only takes 2–4 tasks for the combination of gait and eye movement modalities to obtain an average AUC of 0.97 and accuracy of 0.92.

科技创新2030—“新一代人工智能”重大项目(2022ZD0118002)