Stefan Williams1, Zhibin Zhao2, Awais Hafeez3, David C Wong4, Samuel D Relton5, Hui Fang6, Jane E Alty7. 1. University of Leeds, Leeds Institute of Health Science, Leeds, UK. Electronic address: umswi@leeds.ac.uk. 2. University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, UK; Xi'an Jiatong University, School of Mechanical Engineering, Xi'an, China. 3. University of Leeds, School of Mechanical Engineering, Leeds, UK; University of Engineering and Technology Lahore, Department of Mechatronics and Control Engineering, Lahore, Pakistan. 4. University of Manchester, Division of Informatics, Imaging & Data Sciences, Manchester, UK. 5. University of Leeds, Leeds Institute of Health Science, Leeds, UK. 6. Loughborough University, Department of Computer Science, Loughborough, UK. 7. University of Tasmania, Wicking Dementia Research & Education Centre, Hobart, Australia; Leeds Teaching Hospitals NHS Trust, UK.
Abstract
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.
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'spatients 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.
Authors: Jane Alty; Quan Bai; Renjie Li; Katherine Lawler; Rebecca J St George; Edward Hill; Aidan Bindoff; Saurabh Garg; Xinyi Wang; Guan Huang; Kaining Zhang; Kaylee D Rudd; Larissa Bartlett; Lynette R Goldberg; Jessica M Collins; Mark R Hinder; Sharon L Naismith; David C Hogg; Anna E King; James C Vickers Journal: BMC Neurol Date: 2022-07-18 Impact factor: 2.903
Authors: Adonay S Nunes; Nataliia Kozhemiako; Christopher D Stephen; Jeremy D Schmahmann; Sheraz Khan; Anoopum S Gupta Journal: Front Neurol Date: 2022-02-28 Impact factor: 4.003
Authors: Renjie Li; Rebecca J St George; Xinyi Wang; Katherine Lawler; Edward Hill; Saurabh Garg; Stefan Williams; Samuel Relton; David Hogg; Quan Bai; Jane Alty Journal: Comput Biol Med Date: 2022-06-21 Impact factor: 6.698