dc.contributor.author | Yang, Y-Y | |
dc.contributor.author | Ho, M-Y | |
dc.contributor.author | Tai, C-H | |
dc.contributor.author | Wu, R-M | |
dc.contributor.author | Kuo, M-C | |
dc.contributor.author | Tseng, YJ | |
dc.date.accessioned | 2024-04-12T08:43:36Z | |
dc.date.available | 2024-01-18 | |
dc.date.available | 2024-04-12T08:43:36Z | |
dc.date.issued | 2024-02-08 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/96109 | |
dc.description.abstract | The Motor Disorder Society's Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is designed to assess bradykinesia, the cardinal symptoms of Parkinson's disease (PD). However, it cannot capture the all-day variability of bradykinesia outside the clinical environment. Here, we introduce FastEval Parkinsonism ( https://fastevalp.cmdm.tw/ ), a deep learning-driven video-based system, providing users to capture keypoints, estimate the severity, and summarize in a report. Leveraging 840 finger-tapping videos from 186 individuals (103 patients with Parkinson's disease (PD), 24 participants with atypical parkinsonism (APD), 12 elderly with mild parkinsonism signs (MPS), and 47 healthy controls (HCs)), we employ a dilated convolution neural network with two data augmentation techniques. Our model achieves acceptable accuracies (AAC) of 88.0% and 81.5%. The frequency-intensity (FI) value of thumb-index finger distance was indicated as a pivotal hand parameter to quantify the performance. Our model also shows the usability for multi-angle videos, tested in an external database enrolling over 300 PD patients. | en_US |
dc.format.extent | 31 - ? | |
dc.language | eng | |
dc.publisher | Nature Research | en_US |
dc.relation.ispartof | NPJ Digit Med | |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. | |
dc.title | FastEval Parkinsonism: an instant deep learning-assisted video-based online system for Parkinsonian motor symptom evaluation. | en_US |
dc.type | Article | en_US |
dc.rights.holder | © 2024 The Author(s). Published by Nature Research | |
dc.identifier.doi | 10.1038/s41746-024-01022-x | |
pubs.author-url | https://www.ncbi.nlm.nih.gov/pubmed/38332372 | en_US |
pubs.issue | 1 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published online | en_US |
pubs.volume | 7 | en_US |
dcterms.dateAccepted | 2024-01-18 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |