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Abstract

BackgroundSlowness of movement, known as bradykinesia, is the core clinical sign of Parkinson's and fundamental to its diagnosis. Clinicians commonly assess bradykinesia by making a visual judgement of the patient tapping finger and thumb together repetitively. However, inter-rater agreement of expert assessments has been shown to be only moderate, at best.AimWe propose a low-cost, contactless system using smartphone videos to automatically determine the presence of bradykinesia.MethodsWe collected 70 videos of finger-tap assessments in a clinical setting (40 Parkinson's hands, 30 control hands). Two clinical experts in Parkinson's, blinded to the diagnosis, evaluated the videos to give a grade of bradykinesia severity between 0 and 4 using the Unified Pakinson's Disease Rating Scale (UPDRS). We developed a computer vision approach that identifies regions related to hand motion and extracts clinically-relevant features. Dimensionality reduction was undertaken using principal component analysis before input to classification models (Naïve Bayes, Logistic Regression, Support Vector Machine) to predict no/slight bradykinesia (UPDRS = 0–1) or mild/moderate/severe bradykinesia (UPDRS = 2–4), and presence or absence of Parkinson's diagnosis.ResultsA Support Vector Machine with radial basis function kernels predicted presence of mild/moderate/severe bradykinesia with an estimated test accuracy of 0.8. A Naïve Bayes model predicted the presence of Parkinson's disease with estimated test accuracy 0.67.ConclusionThe method described here presents an approach for predicting bradykinesia from videos of finger-tapping tests. The method is robust to lighting conditions and camera positioning. On a set of pilot data, accuracy of bradykinesia prediction is comparable to that recorded by blinded human experts.

Item Type:	Article
Authors/Creators:	Williams, S and Relton, SD and Fang, H and Alty, J and Qahwaji, R and Graham, CD and Wong, DC
Keywords:	computer vision, artificial intelligence, Parkinson's, bradykinesia, video, diagnosis, support vector machine
Journal or Publication Title:	Artificial Intelligence in Medicine
Publisher:	Elsevier Science Bv
ISSN:	0933-3657
DOI / ID Number:	10.1016/j.artmed.2020.101966
Copyright Information:	© 2020 Elsevier B.V. All rights reserved
Related URLs:	Google Scholar: Alty, J
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