【摘要】 目的 基于语音信号分析和人工智能相结合的无创方法，实现帕金森病与多系统萎缩帕金森型(MSA-P)早期自动化鉴别诊断。方法 2023年7月至2025年2月，于首都医科大学附属北京天坛医院运动障碍性疾病科招募病程<5年的MSA-P患者48例、帕金森病患者76例。设计11种语音任务范式，提取语音信号的声门、发声、构音、韵律、音系特征，以及基于表征学习提取的深度特征，通过数据驱动的方法筛选出最具鉴别力的特征，构建多种机器学习模型，实现对帕金森病与MSA-P患者的分类识别，选择鉴别效能最强的诊断模型。结果 逻辑回归模型表现最佳。对病程<2年的早期患者，帕金森病与MSA-P间的分类准确率92.5%，精确率95.9%，召回率92.2%。在所有病程<5年的患者中，逻辑回归模型准确率89.1%，精确率91.6%，召回率92.4%。即使使用单一语音范式提取的特征进行分析，诊断准确率也可达77.7%。结论 语音信号在帕金森病与MSA-P的早期鉴别诊断中具有重要的应用潜力。更多还原

【Abstract】 Objective To develop an early automated differential diagnosis between Parkinson’s disease(PD) and multiple system atrophy-Parkinsonism(MSA-P) using a non-invasive combination of voice signal analysis and artificial intelligence.Methods From July, 2023 to February, 2025, a total of 48 MSA-P patients and 76 PD patients with a course of less than five years were recruited from Beijing Tiantan Hospital, Capital Medical University. Voice features, such as glottal, phonatory, articulatory, prosodic, phonological and representation learning-based features were extracted from eleven voice tasks. A data-driven approach was used to identify the most discriminative features, which were utilized to construct diagnostic models using a variety of machine learning models. The diagnostic model with the strongest discriminative efficiency was selected.Results The logistic regression model showed the best performance. For early-stage patients with a course less than two years, the diagnostic accuracy, precision and recall rate between PD and MSA-P were 92.5%, 95.9% and 92.2%, respectively. For all the patients with a course less than five years, the logistic regression model achieved an accuracy of 89.1%, a precision of 91.6%, and a recall rate of 92.4%. Even when features extracted from a single speech paradigm were used for analysis, the diagnostic accuracy could still reach 77.7%.Conclusion Voice signals analysis is potential in the early differential diagnosis of PD and MSA-P.更多还原