Dataset Information

Validating an objective video-based dyskinesia severity score in Parkinson's disease patients.

ABSTRACT: Dyskinesia is a common side effect of prolonged dopaminergic therapy in Parkinson's disease patients. Assessing the severity of dyskinesia could help develop better pharmacological and surgical interventions. We have developed a semi-automatic video-based objective dyskinesia quantifying measure called the severity score (SVS) that was evaluated on 35 patient videos. We present a study to evaluate the utility of our severity score and compare its performance to clinical ratings of neurologists. In addition to the Unified Dyskinesia Rating Scale (UDysRS) score for each video, four neurologists provided three sets of time lapsed ratings and rankings of the 35 videos using a specifically developed protocol. The statistical analysis of our data using Kendall's tau-b and intra-class correlations shows that (a) ranking patient videos based on severity is suitable for studying the utility of the SVS, and (b) SVS exhibits moderate utility to quantify dyskinesia severity when compared to manual assessment of dyskinesia by neurologists using the UDysRS. These results support the effective use of SVS as an objective measure to quantify dyskinesia and the rationale for a ranking system that complements traditional rating scales.

SUBMITTER: Rao AS

PROVIDER: S-EPMC4105342 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Validating an objective video-based dyskinesia severity score in Parkinson's disease patients.

Rao Anusha Sathyanarayanan AS Dawant Benoit M BM Bodenheimer Robert E RE Li Rui R Fang John J Phibbs Fenna F Hedera Peter P Davis Thomas T

Parkinsonism & related disorders 20121120 2

Dyskinesia is a common side effect of prolonged dopaminergic therapy in Parkinson's disease patients. Assessing the severity of dyskinesia could help develop better pharmacological and surgical interventions. We have developed a semi-automatic video-based objective dyskinesia quantifying measure called the severity score (SVS) that was evaluated on 35 patient videos. We present a study to evaluate the utility of our severity score and compare its performance to clinical ratings of neurologists. ...[more]

PMID: 23182314

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Project description:BackgroundThe diagnosis of Parkinson's disease (PD) and evaluation of its symptoms require in-person clinical examination. Remote evaluation of PD symptoms is desirable, especially during a pandemic such as the coronavirus disease 2019 pandemic. One potential method to remotely evaluate PD motor impairments is video-based analysis. In this study, we aimed to assess the feasibility of predicting the Unified Parkinson's Disease Rating Scale (UPDRS) score from gait videos using a convolutional neural network (CNN) model.MethodsWe retrospectively obtained 737 consecutive gait videos of 74 patients with PD and their corresponding neurologist-rated UPDRS scores. We utilized a CNN model for predicting the total UPDRS part III score and four subscores of axial symptoms (items 27, 28, 29, and 30), bradykinesia (items 23, 24, 25, 26, and 31), rigidity (item 22) and tremor (items 20 and 21). We trained the model on 80% of the gait videos and used 10% of the videos as a validation dataset. We evaluated the predictive performance of the trained model by comparing the model-predicted score with the neurologist-rated score for the remaining 10% of videos (test dataset). We calculated the coefficient of determination (R2) between those scores to evaluate the model's goodness of fit.ResultsIn the test dataset, the R2 values between the model-predicted and neurologist-rated values for the total UPDRS part III score and subscores of axial symptoms, bradykinesia, rigidity, and tremor were 0.59, 0.77, 0.56, 0.46, and 0.0, respectively. The performance was relatively low for videos from patients with severe symptoms.ConclusionsDespite the low predictive performance of the model for the total UPDRS part III score, it demonstrated relatively high performance in predicting subscores of axial symptoms. The model approximately predicted the total UPDRS part III scores of patients with moderate symptoms, but the performance was low for patients with severe symptoms owing to limited data. A larger dataset is needed to improve the model's performance in clinical settings.

Dataset Information

Validating an objective video-based dyskinesia severity score in Parkinson's disease patients.

Publications

Validating an objective video-based dyskinesia severity score in Parkinson's disease patients.

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