The discerning eye of computer vision: Can it measure Parkinson's finger tap bradykinesia?

JOURNAL OF THE NEUROLOGICAL SCIENCES

卷:416

Article

Williams, Stefan¹  Zhao, Zhibin^2,3  Hafeez, Awais^4,5  Wong, David C.²  Relton, Samuel D.¹  Fang, Hui⁶  Alty, Jane E.^7,8 

[1] Univ Leeds, Leeds Inst Hlth Sci, 10th Floor,Worsley Bldg,Clarendon Way, Leeds LS2 9LU, W Yorkshire, England

[2] Univ Manchester, Div Informat Imaging & Data Sci, Manchester, Lancs, England

[3] Xian Jiatong Univ, Sch Mech Engn, Xian, Peoples R China

[4] Univ Leeds, Sch Mech Engn, Leeds, W Yorkshire, England

[5] Univ Engn & Technol Lahore, Dept Mechatron & Control Engn, Lahore, Pakistan

[6] Loughborough Univ, Dept Comp Sci, Loughborough, Leics, England

[7] Univ Tasmania, Wicking Dementia Res & Educ Ctr, Hobart, Tas, Australia

[8] Leeds Teaching Hosp NHS Trust, Leeds, W Yorkshire, England

关键词: Parkinson's disease;    Parkinsonism;    Bradykinesia;    Finger tapping;    Computer vision;    Artificial intelligence;    DeepLabCut;   

DOI  :  10.1016/j.jns.2020.117003

来源: Elsevier

【 摘 要 】

Objective: The worldwide prevalence of Parkinson's disease is increasing. There is urgent need for new tools to objectively measure the condition. Existing methods to record the cardinal motor feature of the condition, bradykinesia, using wearable sensors or smartphone apps have not reached large-scale, routine use. We evaluate new computer vision (artificial intelligence) technology, DeepLabCut, as a contactless method to quantify measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. Methods: Standard smartphone video recordings of 133 hands performing finger tapping (39 idiopathic Parkinson's patients and 30 controls) were tracked on a frame-by-frame basis with DeepLabCut. Objective computer measures of tapping speed, amplitude and rhythm were correlated with clinical ratings made by 22 movement disorder neurologists using the Modified Bradykinesia Rating Scale (MBRS) and Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Results: DeepLabCut reliably tracked and measured finger tapping in standard smartphone video. Computer measures correlated well with clinical ratings of bradykinesia (Spearman coefficients): -0.74 speed, 0.66 amplitude, -0.65 rhythm for MBRS; -0.56 speed, 0.61 amplitude, -0.50 rhythm for MDS-UPDRS; -0.69 combined for MDS-UPDRS. All p < .001. Conclusion: New computer vision software, DeepLabCut, can quantify three measures related to Parkinson's bradykinesia from smartphone videos of finger tapping. Objective 'contactless' measures of standard clinical examinations were not previously possible with wearable sensors (accelerometers, gyroscopes, infrared markers). DeepLabCut requires only conventional video recording of clinical examination and is entirely 'contactless'. This next generation technology holds potential for Parkinson's and other neurological disorders with altered movements.

【 授权许可】

Free