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Predicting Severity of Huntington's Disease With Wearable Sensors
The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013843/ https://www.ncbi.nlm.nih.gov/pubmed/35445206 http://dx.doi.org/10.3389/fdgth.2022.874208 |
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author | Scheid, Brittany H. Aradi, Stephen Pierson, Robert M. Baldassano, Steven Tivon, Inbar Litt, Brian Gonzalez-Alegre, Pedro |
author_facet | Scheid, Brittany H. Aradi, Stephen Pierson, Robert M. Baldassano, Steven Tivon, Inbar Litt, Brian Gonzalez-Alegre, Pedro |
author_sort | Scheid, Brittany H. |
collection | PubMed |
description | The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must be administered by a trained and experienced rater. An objective, automated method of quantifying disease severity would facilitate superior patient care and could be used to better track severity over time. We conducted the present study to evaluate the feasibility of using wearable sensors, coupled with machine learning algorithms, to rate motor function in patients with HD. Fourteen participants with symptomatic HD and 14 healthy controls participated in the study. Each participant wore five adhesive biometric sensors applied to the trunk and each limb while completing brief walking, sitting, and standing tasks during a single office visit. A two-stage machine learning method was employed to classify participants by HD status and to predict UHDRS motor subscores. Linear discriminant analysis correctly classified all participants' HD status except for one control subject with abnormal gait (96.4% accuracy, 92.9% sensitivity, and 100% specificity in leave-one-out cross-validation). Two regression models accurately predicted individual UHDRS subscores for gait, and dystonia within a 10% margin of error. Our regression models also predicted a composite UHDRS score–a sum of left and right arm rigidity, total chorea, total dystonia, bradykinesia, gait, and tandem gait subscores–with an average error below 15%. Machine learning classifiers trained on brief in-office datasets discriminated between controls and participants with HD, and could accurately predict selected motor UHDRS subscores. Our results could enable the future use of biosensors for objective HD assessment in the clinic or remotely and could inform future studies for the use of this technology as a potential endpoint in clinical trials. |
format | Online Article Text |
id | pubmed-9013843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90138432022-04-19 Predicting Severity of Huntington's Disease With Wearable Sensors Scheid, Brittany H. Aradi, Stephen Pierson, Robert M. Baldassano, Steven Tivon, Inbar Litt, Brian Gonzalez-Alegre, Pedro Front Digit Health Digital Health The Unified Huntington's Disease Rating Scale (UHDRS) is the primary clinical assessment tool for rating motor function in patients with Huntington's disease (HD). However, the UHDRS and similar rating scales (e.g., UPDRS) are both subjective and limited to in-office assessments that must be administered by a trained and experienced rater. An objective, automated method of quantifying disease severity would facilitate superior patient care and could be used to better track severity over time. We conducted the present study to evaluate the feasibility of using wearable sensors, coupled with machine learning algorithms, to rate motor function in patients with HD. Fourteen participants with symptomatic HD and 14 healthy controls participated in the study. Each participant wore five adhesive biometric sensors applied to the trunk and each limb while completing brief walking, sitting, and standing tasks during a single office visit. A two-stage machine learning method was employed to classify participants by HD status and to predict UHDRS motor subscores. Linear discriminant analysis correctly classified all participants' HD status except for one control subject with abnormal gait (96.4% accuracy, 92.9% sensitivity, and 100% specificity in leave-one-out cross-validation). Two regression models accurately predicted individual UHDRS subscores for gait, and dystonia within a 10% margin of error. Our regression models also predicted a composite UHDRS score–a sum of left and right arm rigidity, total chorea, total dystonia, bradykinesia, gait, and tandem gait subscores–with an average error below 15%. Machine learning classifiers trained on brief in-office datasets discriminated between controls and participants with HD, and could accurately predict selected motor UHDRS subscores. Our results could enable the future use of biosensors for objective HD assessment in the clinic or remotely and could inform future studies for the use of this technology as a potential endpoint in clinical trials. Frontiers Media S.A. 2022-04-04 /pmc/articles/PMC9013843/ /pubmed/35445206 http://dx.doi.org/10.3389/fdgth.2022.874208 Text en Copyright © 2022 Scheid, Aradi, Pierson, Baldassano, Tivon, Litt and Gonzalez-Alegre. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Scheid, Brittany H. Aradi, Stephen Pierson, Robert M. Baldassano, Steven Tivon, Inbar Litt, Brian Gonzalez-Alegre, Pedro Predicting Severity of Huntington's Disease With Wearable Sensors |
title | Predicting Severity of Huntington's Disease With Wearable Sensors |
title_full | Predicting Severity of Huntington's Disease With Wearable Sensors |
title_fullStr | Predicting Severity of Huntington's Disease With Wearable Sensors |
title_full_unstemmed | Predicting Severity of Huntington's Disease With Wearable Sensors |
title_short | Predicting Severity of Huntington's Disease With Wearable Sensors |
title_sort | predicting severity of huntington's disease with wearable sensors |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013843/ https://www.ncbi.nlm.nih.gov/pubmed/35445206 http://dx.doi.org/10.3389/fdgth.2022.874208 |
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