用面部视频和深度学习评估帕金森患者抑郁症状，准确率超93%
A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos
- 用ViViT、Video Swin Tiny等模型分析患者面部视频
- 最高达94%准确率，识别抑郁有无；多分类准确率87.1%
- 适合神经科医生做辅助诊断，尤其关注用药状态影响
帕金森病（PD）是一种神经退行性疾病，常伴随运动与非运动症状。抑郁症状在PD患者中极为普遍，影响高达45%的患者，且常因与运动障碍（如表情减少）重叠而被漏诊。本研究探索了深度学习模型——ViViT、Video Swin Tiny及3D CNN-LSTM带注意力层——通过面部视频分析，评估患者抑郁症状的存在与严重程度，以格里特抑郁量表（GDS）为标准。在二次分析中，进一步区分患者服药后（ON状态）与停药12小时后（OFF状态）的情况。基于包含1,875段视频、来自178名患者的数据库，Video Swin Tiny模型表现最佳：二分类任务（有无抑郁）准确率达94%，F1-score为93.7%；多分类任务（无/轻度/重度）准确率为87.1%，F1-score为85.4%。
原文摘要 · Abstract (English)
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).
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