# A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos

arXiv: https://arxiv.org/abs/2505.03845 · HTML: https://mlsanity.com/paper/2505.03845/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 38 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 6 of 6)

- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Synthetic Thermal and RGB Videos for Automatic Pain Assessment utilizing a Vision-MLP Architecture](https://mlsanity.com/paper/2407.19811/) (2024)
- [Interpreting Depression From Question-wise Long-term Video Recording of SDS Evaluation](https://mlsanity.com/paper/2106.13393/) (2021)
- [Exploring Facial Biomarkers for Detecting Depression through Temporal Analysis of Action Units](https://mlsanity.com/paper/2407.13753/) (2024)

## Abstract (arXiv)

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).