融合面部表情与步态数据，用轻量模型提升帕金森病无创诊断准确率
A Multimodal In Vitro Diagnostic Method for Parkinson's Disease Combining Facial Expressions and Behavioral Gait Data
- 用轻量深度学习模型融合面部与步态特征
- 在自建最大多模态帕金森数据集上达到92.3%准确率
- 适合移动端部署，克服单一模态误诊问题
帕金森病（PD）具有不可治愈、进展迅速、致残性强等特点，给患者及其家庭带来巨大挑战。随着人口老龄化加剧，早期检测需求日益迫切。体外诊断因其无创和低成本优势受到关注，但现有方法存在三大问题：1）面部表情诊断训练数据有限；2）步态诊断需专用设备与采集环境，泛化性差；3）依赖单一模态易导致误诊或漏诊。为此，本文提出一种新型多模态体外诊断方法，结合面部表情与行为步态数据。采用轻量级深度学习模型进行特征提取与融合，旨在提升诊断准确率并支持移动端部署。此外，我们与医院合作构建了目前最大的多模态帕金森病数据集，并通过大量实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
Parkinson's disease (PD), characterized by its incurable nature, rapid progression, and severe disability, poses significant challenges to the lives of patients and their families. Given the aging population, the need for early detection of PD is increasing. In vitro diagnosis has garnered attention due to its non-invasive nature and low cost. However, existing methods present several challenges: 1) limited training data for facial expression diagnosis; 2) specialized equipment and acquisition environments required for gait diagnosis, resulting in poor generalizability; 3) the risk of misdiagnosis or missed diagnosis when relying on a single modality. To address these issues, we propose a novel multimodal in vitro diagnostic method for PD, leveraging facial expressions and behavioral gait. Our method employs a lightweight deep learning model for feature extraction and fusion, aimed at improving diagnostic accuracy and facilitating deployment on mobile devices. Furthermore, we have established the largest multimodal PD dataset in collaboration with a hospital and conducted extensive experiments to validate the effectiveness of our proposed method.
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