Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease
The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson’s Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent cha...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/22/9149 |
| _version_ | 1797457759020515328 |
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| author | Tianze Yu Kye Won Park Martin J. McKeown Z. Jane Wang |
| author_facet | Tianze Yu Kye Won Park Martin J. McKeown Z. Jane Wang |
| author_sort | Tianze Yu |
| collection | DOAJ |
| description | The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson’s Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson’s Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future. |
| first_indexed | 2024-03-09T16:28:33Z |
| format | Article |
| id | doaj.art-8bb5191a9a894370b6006d9105acc706 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T16:28:33Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8bb5191a9a894370b6006d9105acc7062023-11-24T15:05:31ZengMDPI AGSensors1424-82202023-11-012322914910.3390/s23229149Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s DiseaseTianze Yu0Kye Won Park1Martin J. McKeown2Z. Jane Wang3Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaPacific Parkinson Research Centre, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaPacific Parkinson Research Centre, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, CanadaThe utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson’s Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson’s Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future.https://www.mdpi.com/1424-8220/23/22/9149Parkinson’s diseasefinger tappingUDPRS quantificationdata-drivenmachine learning |
| spellingShingle | Tianze Yu Kye Won Park Martin J. McKeown Z. Jane Wang Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease Sensors Parkinson’s disease finger tapping UDPRS quantification data-driven machine learning |
| title | Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease |
| title_full | Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease |
| title_fullStr | Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease |
| title_full_unstemmed | Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease |
| title_short | Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson’s Disease |
| title_sort | clinically informed automated assessment of finger tapping videos in parkinson s disease |
| topic | Parkinson’s disease finger tapping UDPRS quantification data-driven machine learning |
| url | https://www.mdpi.com/1424-8220/23/22/9149 |
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