Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores in <i>de novo</i> patients using smartphone assessments

doi:10.1177/1877718X251359494

	*Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores in de novo* patients using smartphone assessments**
	Guo, Wei-Hang 1; Yang, Xiao-Dong 2,3; Ruan, Zheng 1; Wang, Xu 4; Zhang, Dan-Zuo 2,3; Song, Shu-Chao 2,3; Chen, Yi-Qiang 2,3; Chan, Piu 1,5,6,7
	2025-07-28
发表期刊	JOURNAL OF PARKINSONS DISEASE
ISSN	1877-7171
页码	12
摘要	Background: Detecting motor symptoms in Parkinson's disease (PD) at home, especially in the prodromal, is crucial for disease-modifying therapies. Objective: To evaluate the effectiveness of machine learning models using smartphone-based assessments in predicting motor symptoms in untreated de novo PD. Methods: Using a clinical trial in early de novo patients with PD, the PDAssist smartphone application and machine learning models were investigated for eight motor tasks: resting tremor, postural tremor, finger tapping, facial expressions, rigidity, speech, walking, and pronation/supination to predict motor symptoms of PD as comparing with UPDRS Part III scores. Results: Our prediction model demonstrated acceptable performance in detecting PD mild symptoms, with accuracy ranging from 0.87 to 0.93 for resting tremor, postural tremor, finger tapping, facial expressions and postural stability, while the rigidity model achieved 0.81 accuracy with a Kappa of 0.74, and the speech model showed 0.79 accuracy and 0.61 Kappa, emphasizing its potential for detecting subtle motor deficits and remote monitoring. External validation confirmed the model's robustness, with significantly higher predicted scores (all tasks) for PD patients (9.45 +/- 3.08) compared to healthy controls (3.79 +/- 1.99, t = -14.27, p < 0.001), validating its ability to differentiate between the two groups. Conclusions: Smartphone-based assessments effectively discriminate de novo PD patients from controls and monitor motor symptoms in prodromal and early PD patients. Future work will involve expanding patient cohorts and refining algorithms for better generalizability and reliability of self-collected data in home settings. Plain language summary UPDRS Part III score has been suggested a sensitive measure for detecting early motor symptoms of Parkinson's disease (PD), but it is difficult to apply at home setting which is important for early detection and intervention. Smartphone apps and machine learning models may provide the alternative. Taking the advantage of a clinical trial in early de novo PD patients, we used a smartphone app, PDAssist, to investigate the machine learning models on various motor symptoms including tremors, finger tapping, facial expressions, rigidity, speech, walking, and pronation/supination as compared with their UPDRS Part III score. The PDAssist apps performed well in identifying mild motor symptoms like tremors finger tapping and facial expression with high accuracy in measurements. These results suggest that smartphone-based assessments can be useful tools for identifying early motor deficits in de novo PD patients and monitoring of PD motor symptoms from home.
关键词	Parkinson's disease UPDRS part III machine learning smartphone assessment untreated de novo patients motor symptoms
DOI	10.1177/1877718X251359494
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Plan of China[2021YFC2501202] ; National Natural Science Foundation of China[62202455] ; Beijing Municipal Science & Technology Commission[Z221100002722009]
WOS研究方向	Neurosciences & Neurology
WOS类目	Neurosciences
WOS记录号	WOS:001538855200001
出版者	SAGE PUBLICATIONS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42053
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chen, Yi-Qiang; Chan, Piu
作者单位	1.Capital Med Univ, Xuanwu Hosp, Dept Neurol & Neurobiol, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Devices, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Jinan Zhongke Ubiquitous Intelligent Comp Res Inst, Beijing, Peoples R China 5.Capital Med Univ, Xuanwu Hosp, Key Lab Neurodegenerat Disorders, Minist Educ, Beijing, Peoples R China 6.Capital Med Univ, Xuanwu Hosp, Key Lab Parkinsons Dis Beijing, Beijing, Peoples R China 7.Capital Med Univ, Xuanwu Hosp, Parkinsons Dis Ctr Beijing Inst Brain Disorders, Collaborat Innovat Ctr Brain Disorders, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Guo, Wei-Hang,Yang, Xiao-Dong,Ruan, Zheng,et al. Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores in de novo patients using smartphone assessments[J]. JOURNAL OF PARKINSONS DISEASE,2025:12.
APA	Guo, Wei-Hang.,Yang, Xiao-Dong.,Ruan, Zheng.,Wang, Xu.,Zhang, Dan-Zuo.,...&Chan, Piu.(2025).Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores in de novo patients using smartphone assessments.JOURNAL OF PARKINSONS DISEASE,12.
MLA	Guo, Wei-Hang,et al."Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores in de novo patients using smartphone assessments".JOURNAL OF PARKINSONS DISEASE (2025):12.