Interpretable video-based tracking and quantification of parkinsonism clinical motor states.

ABSTRACT: Quantification of motor symptom progression in Parkinson's disease (PD) patients is crucial for assessing disease progression and for optimizing therapeutic interventions, such as dopaminergic medications and deep brain stimulation. Cumulative and heuristic clinical experience has identified various clinical signs associated with PD severity, but these are neither objectively quantifiable nor robustly validated. Video-based objective symptom quantification enabled by machine learning (ML) introduces a potential solution. However, video-based diagnostic tools often have implementation challenges due to expensive and inaccessible technology, and typical "black-box" ML implementations are not tailored to be clinically interpretable. Here, we address these needs by releasing a comprehensive ki

SUBMITTER: Deng D 

PROVIDER: S-EPMC11199701 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature