Deep learning-enabled accurate assessment of gait impairments in Parkinson's disease using smartphone videos.

ABSTRACT: Gait impairments are among the most prevalent and disabling symptoms in Parkinson's Disease (PD), featuring complex and highly heterogeneous manifestations. Here, we propose a deep learning-based framework to assess gait impairments using smartphone-recorded videos. This framework demonstrated high proficiency in predicting PD severity, with a micro-average area under the receiver operating characteristic curve (AUC) of 0.87 and an F1 score of 0.806, comparable to the average performance of three clinical specialists. Additionally, it effectively discerned the comprehensive efficacy of medications on gait impairments with a precision of 73.68%. In particular, it demonstrated the ability to discriminate medication-induced fine-granular gait changes beyond the resolution of the Unified Parki

SUBMITTER: Han J 

PROVIDER: S-EPMC12855914 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature