Video-Based Analyses of Parkinson’s Disease Severity: A Brief Review

Sibley, Krista; Girges, Christine; Hoque, Ehsan; Foltynie, Thomas

doi:10.3233/jpd-202402

Cited by 43 publications

(46 citation statements)

References 38 publications

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“…These findings should encourage further research to focus on meticulous detection methods of motor dysfunction throughout the disease course, including the prodromal phase of PD. In fact, a recent review gave evidence about the potential role of video-based artificial intelligence in PD diagnosis and monitoring which could be particularly useful when classification involves complex and dynamical patterns of movement [29].…”

Section: Discussionmentioning

confidence: 99%

Slow Motion Analysis of Repetitive Tapping (SMART) Test: Measuring Bradykinesia in Recently Diagnosed Parkinson’s Disease and Idiopathic Anosmia

Simonet

Galmés

Lambert

et al. 2021

JPD

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Background: Bradykinesia is the defining motor feature of Parkinson’s disease (PD). There are limitations to its assessment using standard clinical rating scales, especially in the early stages of PD when a floor effect may be observed. Objective: To develop a quantitative method to track repetitive tapping movements and to compare people in the early stages of PD, healthy controls, and individuals with idiopathic anosmia. Methods: This was a cross-sectional study of 99 participants (early-stage PD = 26, controls = 64, idiopathic anosmia = 9). For each participant, repetitive finger tapping was recorded over 20 seconds using a smartphone at 240 frames per second. From each video, amplitude between fingers, frequency (number of taps per second), and velocity (distance travelled per second) was extracted. Clinical assessment was based on the motor section of the MDS-UPDRS. Results: People in the early stage of PD performed the task with slower velocity (p < 0.001) and with greater frequency slope than controls (p = 0.003). The combination of reduced velocity and greater frequency slope obtained the best accuracy to separate early-stage PD from controls based on metric thresholds alone (AUC = 0.88). Individuals with anosmia exhibited slower velocity (p = 0.001) and smaller amplitude (p < 0.001) compared with controls. Conclusion: We present a simple, proof-of-concept method to detect early motor dysfunction in PD. Mean tap velocity appeared to be the best parameter to differentiate patients with PD from controls. Patients with anosmia also showed detectable differences in motor performance compared with controls which may suggest that some are in the prodromal phase of PD.

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Section: Discussionmentioning

confidence: 99%

Slow Motion Analysis of Repetitive Tapping (SMART) Test: Measuring Bradykinesia in Recently Diagnosed Parkinson’s Disease and Idiopathic Anosmia

Simonet

Galmés

Lambert

et al. 2021

JPD

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“…Different video-based systems have been described for the automated assessment of upper limb motor performance in the patients with PD ( 10 ). Most of these studies are restricted to the performance of one single motor task of the MDS-UPDRS-III with scarce comparisons of the motor performance between the different body sides ( 10 , 11 ).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…The optical motion capture systems based on video processing can also be used to study motor performance ( 8 , 9 ). Specifically, some video-based systems are developed for the automated assessment of the upper limb movement in the patients with PD ( 10 ). These systems included cameras combined with the colored and reflective markers, bare hand tracking by the depth-sensing devices that traced the upper limb movement while performing the MDS-UPDRS part III bradykinesia tasks ( 11 – 13 ).…”

Section: Introductionmentioning

confidence: 99%

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Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence

et al. 2021

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Objective: This study aimed to prove the concept of a new optical video-based system to measure Parkinson's disease (PD) remotely using an accessible standard webcam.Methods: We consecutively enrolled a cohort of 42 patients with PD and healthy subjects (HSs). The participants were recorded performing MDS-UPDRS III bradykinesia upper limb tasks with a computer webcam. The video frames were processed using the artificial intelligence algorithms tracking the movements of the hands. The video extracted features were correlated with clinical rating using the Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale and inertial measurement units (IMUs). The developed classifiers were validated on an independent dataset.Results: We found significant differences in the motor performance of the patients with PD and HSs in all the bradykinesia upper limb motor tasks. The best performing classifiers were unilateral finger tapping and hand movement speed. The model correlated both with the IMUs for quantitative assessment of motor function and the clinical scales, hence demonstrating concurrent validity with the existing methods.Conclusions: We present here the proof-of-concept of a novel webcam-based technology to remotely detect the parkinsonian features using artificial intelligence. This method has preliminarily achieved a very high diagnostic accuracy and could be easily expanded to other disease manifestations to support PD management.

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“…And, of course, the virtual visit is significant by reducing the risk of COVID-19 infection, especially for older patients with PD who are considered a high-risk group. 37 During screening and instruction delivery before the virtual assessment, several patients selected not to participate because of technological challenges. In future studies, relative information about the virtual assessment and application installation could be delivered preoperatively to avoid these problems.…”

Section: Discussionmentioning

confidence: 99%

Remote video-based outcome measures of patients with Parkinson’s disease after deep brain stimulation using smartphones: a pilot study

Zeng

et al. 2021

Neurosurgical Focus

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OBJECTIVE To provide better postoperative healthcare for patients with Parkinson’s disease (PD) who received deep brain stimulation (DBS) surgery and to allow surgeons improved tracking of surgical outcomes, the authors sought to examine the applicability and feasibility of remote assessment using smartphones. METHODS A disease management mobile application specifically for PD was used to perform the remote assessment of patients with PD who underwent DBS. Connection with patients was first established via a phone call or a social application, and instructions for completing the remote assessment were delivered. During the video-based virtual meeting, three nonmotor assessment scales measuring the quality of life and mental state, and a modified version of the Movement Disorder Society–sponsored revision of the Unified Parkinson’s Disease Rating Scale, part III (MDS-UPDRS III) measuring motor abilities were evaluated. After the assessment, a report and the satisfaction questionnaire were sent to the patient. RESULTS Overall, 22 patients were recruited over a 4-week period. Among those, 18 patients completed the assessment on the mobile application. The mean duration was 41.3 minutes for video assessment and 17.5 minutes for nonmotor assessment via telephone. The mean estimated cost was 427.68 Chinese yuan (CNY) for an in-person visit and 20.91 CNY for a virtual visit (p < 0.001). The mean time estimate for an in-person visit was 5.51 hours and 0.68 hours for a virtual visit (p = 0.002). All patients reported satisfaction (77.78% very satisfied and 22.22% satisfied) with the virtual visit and were specifically impressed by the professionalism and great attitude of the physician assistant. The majority of patients agreed that the evaluation time was reasonable (50% totally agree, 44.44% agree, and 5.56% neither agree nor disagree) and all patients expressed interest in future virtual visits (61.11% very willingly and 38.89% willingly). No adverse events were observed during the virtual visit. CONCLUSIONS Innovation in remote assessment technologies was highly feasible for its transforming power in the clinical management of patients with PD who underwent DBS and research. Video-based remote assessment offered considerable time and resource reduction for both patients and doctors. It also increased safety and was a well-accepted, favored tool. Finally, the results of this study have shown there is potential to combine remote assessment tools with real-life clinical visits and other telemedical technologies to collectively benefit the postoperative healthcare of patients with PD undergoing DBS.

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Video-Based Analyses of Parkinson’s Disease Severity: A Brief Review

Cited by 43 publications

References 38 publications

Slow Motion Analysis of Repetitive Tapping (SMART) Test: Measuring Bradykinesia in Recently Diagnosed Parkinson’s Disease and Idiopathic Anosmia

Slow Motion Analysis of Repetitive Tapping (SMART) Test: Measuring Bradykinesia in Recently Diagnosed Parkinson’s Disease and Idiopathic Anosmia

Remote Evaluation of Parkinson's Disease Using a Conventional Webcam and Artificial Intelligence

Remote video-based outcome measures of patients with Parkinson’s disease after deep brain stimulation using smartphones: a pilot study

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