Differential diagnosis between Parkinson's disease and atypical parkinsonism based on gait and postural instability: Artificial intelligence using an enhanced weight voting ensemble model

Background: Parkinsonian diseases and cerebellar ataxia among movement disorders, are representative diseases which present with distinct pathological gaits. We proposed a machine learning system that can differentiate Parkinson's disease (PD), cerebellar ataxia and progressive supranuclear palsy Richardson syndrome (PSP-RS) based on postural instability and gait analysis.

Methods: We screened 1467 gait (GAITRite) and postural instability (Pedoscan) analyses performed in Samsung Medical Center from January 2019 to December 2020. PD, probable PSP-RS, and cerebellar ataxia (i.e., probable MSA-C, hereditary ataxia, and sporadic adult-onset ataxia) were included in the study. The gated recurrent units for GaitRite and the deep neural network for Pedoscan were applied. The enhanced weight voting ensemble (EWVE) method was applied to incorporate the two modalities.

Results: We included 551 PD, 38 PSP-RS, 113 cerebellar ataxia and among them, 71 were MSA-C. Pedoscan-based and Gait-based model showed high sensitivity but low specificity in differentiating atypical parkinsonism from PD. The EWVE showed significantly improved specificity and reliable performance in differentiation between PD vs. ataxia patients (AUC 0.974 ± 0.036, sensitivity 0.829 ± 0.217, specificity 0.969 ± 0.038), PD vs. MSA-C (AUC 0.975 ± 0.020, sensitivity 0.823 ± 0.162, specificity 0.932 ± 0.030) and PD vs. PSP-RS (AUC 0.963 ± 0.028, sensitivity 0.555 ± 0.157, specificity 0.936 ± 0.031).

Conclusion: We proposed reliable Pedoscan-based, Gait-based and EWVE model in differentiating gait disorders by integrating information from gait and postural instability. This model can provide diagnosis guidelines to primary caregivers and assist in differential diagnosis of PD from atypical parkinsonism for neurologists.