Yang, J., Williams, S., Hogg, D.C. orcid.org/0000-0002-6125-9564 et al. (2 more authors) (2024) Deep learning of Parkinson's movement from video, without human-defined measures. Journal of the Neurological Sciences, 463. 123089. ISSN 0022-510X
Abstract
Background
The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures.
Objectives
(1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model.
Methods
152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips.
Results
For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group.
Conclusions
A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/). |
Keywords: | Parkinson's disease; Bradykinesia; Computer vision; Video; Deep learning; Artificial intelligence |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Jun 2024 11:39 |
Last Modified: | 22 Oct 2024 14:56 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.jns.2024.123089 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213379 |