Journal of neurology · 2026
Balance biomarker for early differentiation of Parkinson's disease and multiple system atrophy with parkinsonian type.
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Chang Hee Jin, Kim Jong Ho, Song Han-Wook, Lee Sanghyun, Kwon Eunjin, Jeong Seong-Hae, Oh Eungseok
Аннотация
BACKGROUND: Parkinson's disease (PD) and multiple system atrophy with parkinsonian type (MSA-P) share various motor and nonmotor symptoms, complicating early differential diagnosis, although prognosis and levodopa response differ.
OBJECTIVES: To develop a machine learning model using balance analysis to differentiate early-stage PD and MSA-P.
METHODS: We enrolled 22 healthy controls (HC), 20 PD, and 17 MSA-P patients within three years of onset. Participants stood for 60 s on dual force-plates under eyes open (EO) and eyes closed (EC). Seven center of pressure (COP) parameters per condition and EC-EO differences were analyzed. Group differences were assessed with ANOVA. Feature selection was performed using LightGBM (LGBM), followed by model training with fourfold cross-validation.
RESULTS: Mean ages were 63.8 (HC), 64.2 (PD), and 68.8 years (MSA-P). Disease durations were 2.4 years in PD and 1.8 years in MSA-P. Except for mean distance in the anteroposterior direction with EO and 95% confidence ellipse area with EC, all parameters showed significant group differences. Post-hoc analysis revealed significant differences between HC and PD/MSA-P in EO, whereas in EC, differences were more pronounced between MSA-P and PD/HC. The LGBM model achieved 81.4% accuracy in distinguishing PD from MSA-P, 94.9% in distinguishing healthy controls from patient groups, and 84.9% for three-group classification.
CONCLUSION: Balance biomarker, which analyzes the balance parameters with machine learning model could differentiate early-stage PD and MSA-P with high accuracy.
OBJECTIVES: To develop a machine learning model using balance analysis to differentiate early-stage PD and MSA-P.
METHODS: We enrolled 22 healthy controls (HC), 20 PD, and 17 MSA-P patients within three years of onset. Participants stood for 60 s on dual force-plates under eyes open (EO) and eyes closed (EC). Seven center of pressure (COP) parameters per condition and EC-EO differences were analyzed. Group differences were assessed with ANOVA. Feature selection was performed using LightGBM (LGBM), followed by model training with fourfold cross-validation.
RESULTS: Mean ages were 63.8 (HC), 64.2 (PD), and 68.8 years (MSA-P). Disease durations were 2.4 years in PD and 1.8 years in MSA-P. Except for mean distance in the anteroposterior direction with EO and 95% confidence ellipse area with EC, all parameters showed significant group differences. Post-hoc analysis revealed significant differences between HC and PD/MSA-P in EO, whereas in EC, differences were more pronounced between MSA-P and PD/HC. The LGBM model achieved 81.4% accuracy in distinguishing PD from MSA-P, 94.9% in distinguishing healthy controls from patient groups, and 84.9% for three-group classification.
CONCLUSION: Balance biomarker, which analyzes the balance parameters with machine learning model could differentiate early-stage PD and MSA-P with high accuracy.