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Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?

Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person’s functional recovery status...

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Autores principales: Emmerzaal, Jill, De Brabandere, Arne, van der Straaten, Rob, Bellemans, Johan, De Baets, Liesbet, Davis, Jesse, Jonkers, Ilse, Timmermans, Annick, Vanwanseele, Benedicte
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143351/
https://www.ncbi.nlm.nih.gov/pubmed/35632107
http://dx.doi.org/10.3390/s22103698
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author Emmerzaal, Jill
De Brabandere, Arne
van der Straaten, Rob
Bellemans, Johan
De Baets, Liesbet
Davis, Jesse
Jonkers, Ilse
Timmermans, Annick
Vanwanseele, Benedicte
author_facet Emmerzaal, Jill
De Brabandere, Arne
van der Straaten, Rob
Bellemans, Johan
De Baets, Liesbet
Davis, Jesse
Jonkers, Ilse
Timmermans, Annick
Vanwanseele, Benedicte
author_sort Emmerzaal, Jill
collection PubMed
description Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person’s functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model’s output be used to monitor a person’s recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model’s output and patient-reported functioning. Thus, the LR-model’s output can be used as a screening tool to follow-up a person’s recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation.
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spelling pubmed-91433512022-05-29 Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty? Emmerzaal, Jill De Brabandere, Arne van der Straaten, Rob Bellemans, Johan De Baets, Liesbet Davis, Jesse Jonkers, Ilse Timmermans, Annick Vanwanseele, Benedicte Sensors (Basel) Article Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models (probability of belonging to a class) is usable for monitoring a person’s functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model’s output be used to monitor a person’s recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model’s output and patient-reported functioning. Thus, the LR-model’s output can be used as a screening tool to follow-up a person’s recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation. MDPI 2022-05-12 /pmc/articles/PMC9143351/ /pubmed/35632107 http://dx.doi.org/10.3390/s22103698 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Emmerzaal, Jill
De Brabandere, Arne
van der Straaten, Rob
Bellemans, Johan
De Baets, Liesbet
Davis, Jesse
Jonkers, Ilse
Timmermans, Annick
Vanwanseele, Benedicte
Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?
title Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?
title_full Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?
title_fullStr Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?
title_full_unstemmed Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?
title_short Can the Output of a Learned Classification Model Monitor a Person’s Functional Recovery Status Post-Total Knee Arthroplasty?
title_sort can the output of a learned classification model monitor a person’s functional recovery status post-total knee arthroplasty?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143351/
https://www.ncbi.nlm.nih.gov/pubmed/35632107
http://dx.doi.org/10.3390/s22103698
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