An explainable spatial-temporal graphical convolutional network to score freezing of gait in parkinsonian patients.

ABSTRACT: Freezing of gait (FOG) is a poorly understood heterogeneous gait disorder seen in patients with parkinsonism which contributes to significant morbidity and social isolation. FOG is currently measured with scales that are typically performed by movement disorders specialists (ie. MDS-UPDRS), or through patient completed questionnaires (N-FOG-Q) both of which are inadequate in addressing the heterogeneous nature of the disorder and are unsuitable for use in clinical trials The purpose of this study was to devise a method to measure FOG objectively, hence improving our ability to identify it and accurately evaluate new therapies. We trained interpretable deep learning models with multi-task learning to simultaneously score FOG (cross-validated F1 score 97.6%), identify medication state (OFF v

SUBMITTER: Kwon H 

PROVIDER: S-EPMC9882551 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature