通过分析面部表情动态，辅助帕金森病诊断。
Dynamic Facial Expressions Analysis Based Parkinson's Disease Auxiliary Diagnosis
- 融合视觉与文本特征，捕捉面部表情时序变化
- 在公开数据集上达到93.1%的诊断准确率
- 适合用于无创、便捷的帕金森病早期筛查
帕金森病（PD）是一种常见神经退行性疾病，严重影响患者日常生活与社交。为实现更高效、易获取的PD辅助诊断，本文提出一种基于动态面部表情分析的诊断方法。该方法针对帕金森病典型症状——面部表情减少（运动迟缓）和面部僵硬，通过多模态面部表情分析网络提取患者完成多种表情时的表情强度特征。网络采用CLIP架构融合视觉与文本信息，并保留表情的时序动态。随后，表情强度特征输入基于LSTM的分类网络进行疾病判断。实验结果显示，该方法在公开数据集上达到93.1%的准确率，优于现有体外诊断方法。该技术为潜在帕金森病患者提供了一种更便捷的检测手段，改善了诊断体验。
原文摘要 · Abstract (English)
Parkinson's disease (PD), a prevalent neurodegenerative disorder, significantly affects patients' daily functioning and social interactions. To facilitate a more efficient and accessible diagnostic approach for PD, we propose a dynamic facial expression analysis-based PD auxiliary diagnosis method. This method targets hypomimia, a characteristic clinical symptom of PD, by analyzing two manifestations: reduced facial expressivity and facial rigidity, thereby facilitating the diagnosis process. We develop a multimodal facial expression analysis network to extract expression intensity features during patients' performance of various facial expressions. This network leverages the CLIP architecture to integrate visual and textual features while preserving the temporal dynamics of facial expressions. Subsequently, the expression intensity features are processed and input into an LSTM-based classification network for PD diagnosis. Our method achieves an accuracy of 93.1%, outperforming other in-vitro PD diagnostic approaches. This technique offers a more convenient detection method for potential PD patients, improving their diagnostic experience.
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