解放军医学院学报2025，Vol.46Issue(7)：638-645,8.DOI:10.12435/j.issn.2095-5227.25010501
基于RTMPose和PatchTST的帕金森病和特发性震颤的视频鉴别诊断研究
Video-based differential diagnosis of Parkinson's disease and essential tremor using RTMPose and PatchTST
摘要
Abstract
Background Parkinson's disease(PD)and essential tremor(ET)share overlapping clinical manifestations.Current diagnostic approaches rely on subjective rating scales by neurologists,which are time-consuming and limited inter-rater consistency.Objective To develop an intelligent classification model based on video analysis,integrating deep learning to achieve efficient and automated differentiation between PD and ET,thereby offering a novel approach for non-invasive diagnosis.Methods A total of 14 PD and 63 ET patients from the Outpatient Department of PLA General Hospital(Nov.2021 to Jan.2024)were enrolled.A dataset comprising 1 136 video clips was collected during the performance of three standardized upper limb motor tasks:finger-to-nose,hand pronation-supination,and fist opening-closing.Using the RTMPose model within the MMPose framework,keypoint coordinates of the wrist and fingers were extracted.Kinematic features such as displacement,velocity,and acceleration were computed to construct a dataset of spatiotemporal trajectories and statistical descriptors.A Transformer-based PatchTST model was developed,in which temporal sequences were segmented into patches and processed via global attention mechanisms.Model performance was compared against logistic regression,XGBoost,random forest,support vector machine,Informer,and long short-term memory(LSTM)networks.Results The PatchTST model achieved the best average performance when combining keypoint coordinates with kinematic features.The highest accuracy was observed in the finger-to-nose task(AUC=0.957),with an overall average AUC of 0.897 among all three tasks.Among the 21 model-feature combinations,the LSTM model using only kinematic features performed the worst,with an average AUC of 0.691.Conclusion The video-based intelligent differential diagnosis method for PD and ET,leveraging human pose estimation and deep learning technologies,enables high-precision and high-efficiency remote diagnosis without physical contact,providing reference for early diagnosis and management of movement disorders.关键词
帕金森病/特发性震颤/视频诊断/人体姿态估计/深度学习/Transformer模型/远程医疗Key words
Parkinson's disease/essential tremor/video-based diagnosis/human pose estimation/deep learning/transformer model/telemedicine分类
医药卫生引用本文复制引用
彭宇盟,于金泽,潘隆盛,曾梓敬,袁田,时颖,张政波..基于RTMPose和PatchTST的帕金森病和特发性震颤的视频鉴别诊断研究[J].解放军医学院学报,2025,46(7):638-645,8.基金项目
北京市自然科学基金资助项目(7252299) （7252299）
