A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos
Journal:
arXiv
Published Date:
May 5, 2025
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder, manifesting with
motor and non-motor symptoms. Depressive symptoms are prevalent in PD,
affecting up to 45% of patients. They are often underdiagnosed due to
overlapping motor features, such as hypomimia. This study explores deep
learning (DL) models-ViViT, Video Swin Tiny, and 3D CNN-LSTM with attention
layers-to assess the presence and severity of depressive symptoms, as detected
by the Geriatric Depression Scale (GDS), in PD patients through facial video
analysis. The same parameters were assessed in a secondary analysis taking into
account whether patients were one hour after (ON-medication state) or 12 hours
without (OFF-medication state) dopaminergic medication. Using a dataset of
1,875 videos from 178 patients, the Video Swin Tiny model achieved the highest
performance, with up to 94% accuracy and 93.7% F1-score in binary
classification (presence of absence of depressive symptoms), and 87.1% accuracy
with an 85.4% F1-score in multiclass tasks (absence or mild or severe
depressive symptoms).
