Objective To develop an early automated differential diagnosis between Parkinson's disease(PD)and multiple system at-rophy-Parkinsonism(MSA-P)using a non-invasive combination of voice signal analysis and artificial intelli-gence.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 glot-tal,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 accu-racy 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.

Objective To develop an early automated differential diagnosis between Parkinson's disease(PD)and multiple system at-rophy-Parkinsonism(MSA-P)using a non-invasive combination of voice signal analysis and artificial intelli-gence.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 glot-tal,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 accu-racy 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.