Decision Tree Learning Algorithm for Classifying Knee Injury Status Using Return-to-Activity Criteria
(2020) 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2020-July. p.5494-5497- Abstract
Anterior cruciate ligament (ACL) injury rates in female adolescents are increasing. Irrespective of treatment options, approximately 1/3 will suffer secondary ACL injuries following their return to activity (RTA). Despite this, there are no evidence-informed RTA guidelines to aid clinicians in deciding when this should occur. The first step towards these guidelines is to identify relevant and feasible measures to assess the functional status of these patients. The purpose of this study was therefore to evaluate tests frequently used to assess functional capacity following surgery using a Reduced Error Pruning Tree (REPT). Thirty-six healthy and forty-two ACLinjured adolescent females performed a series of functional tasks. Motion... (More)
Anterior cruciate ligament (ACL) injury rates in female adolescents are increasing. Irrespective of treatment options, approximately 1/3 will suffer secondary ACL injuries following their return to activity (RTA). Despite this, there are no evidence-informed RTA guidelines to aid clinicians in deciding when this should occur. The first step towards these guidelines is to identify relevant and feasible measures to assess the functional status of these patients. The purpose of this study was therefore to evaluate tests frequently used to assess functional capacity following surgery using a Reduced Error Pruning Tree (REPT). Thirty-six healthy and forty-two ACLinjured adolescent females performed a series of functional tasks. Motion analysis along with spatiotemporal measures were used to extract thirty clinically relevant variables. The REPT reduced these variables down to two limb symmetry measures (maximum anterior hop and maximum lateral hop), capable of classifying injury status between the healthy and ACL injured participants with a 69% sensitivity, 78% specificity and kappa statistic of 0.464. We, therefore, conclude that the REPT model was able to evaluate functional capacity as it relates to injury status in adolescent females. We also recommend considering these variables when developing RTA assessments and guidelines.Clinical Relevance - Our results indicate that spatiotemporal measures may differentiate ACL-injured and healthy female adolescents with moderate confidence using a REPT. The identified tests may reasonably be added to the clinical evaluation process when evaluating functional capacity and readiness to return to activity.
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- author
- Girard, Celine I. ; Warren, Claire E. ; Romanchuk, Nicholas J. ; Del Bel, Michael J. ; Carsen, Sasha ; Chan, Adrian D.C. and Benoit, Daniel L. LU
- publishing date
- 2020-07
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- host publication
- 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : Enabling Innovative Technologies for Global Healthcare, EMBC 2020 - Enabling Innovative Technologies for Global Healthcare, EMBC 2020
- series title
- Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
- volume
- 2020-July
- article number
- 9176010
- pages
- 5494 - 5497
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
- conference location
- Montreal, Canada
- conference dates
- 2020-07-20 - 2020-07-24
- external identifiers
-
- scopus:85091046519
- ISSN
- 1557-170X
- ISBN
- 9781728119908
- DOI
- 10.1109/EMBC44109.2020.9176010
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2020 IEEE.
- id
- d643eb3d-0fb5-4104-8a8e-cc363271f508
- date added to LUP
- 2023-08-24 16:32:21
- date last changed
- 2023-08-25 14:02:51
@inproceedings{d643eb3d-0fb5-4104-8a8e-cc363271f508,
abstract = {{<p>Anterior cruciate ligament (ACL) injury rates in female adolescents are increasing. Irrespective of treatment options, approximately 1/3 will suffer secondary ACL injuries following their return to activity (RTA). Despite this, there are no evidence-informed RTA guidelines to aid clinicians in deciding when this should occur. The first step towards these guidelines is to identify relevant and feasible measures to assess the functional status of these patients. The purpose of this study was therefore to evaluate tests frequently used to assess functional capacity following surgery using a Reduced Error Pruning Tree (REPT). Thirty-six healthy and forty-two ACLinjured adolescent females performed a series of functional tasks. Motion analysis along with spatiotemporal measures were used to extract thirty clinically relevant variables. The REPT reduced these variables down to two limb symmetry measures (maximum anterior hop and maximum lateral hop), capable of classifying injury status between the healthy and ACL injured participants with a 69% sensitivity, 78% specificity and kappa statistic of 0.464. We, therefore, conclude that the REPT model was able to evaluate functional capacity as it relates to injury status in adolescent females. We also recommend considering these variables when developing RTA assessments and guidelines.Clinical Relevance - Our results indicate that spatiotemporal measures may differentiate ACL-injured and healthy female adolescents with moderate confidence using a REPT. The identified tests may reasonably be added to the clinical evaluation process when evaluating functional capacity and readiness to return to activity.</p>}},
author = {{Girard, Celine I. and Warren, Claire E. and Romanchuk, Nicholas J. and Del Bel, Michael J. and Carsen, Sasha and Chan, Adrian D.C. and Benoit, Daniel L.}},
booktitle = {{42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society : Enabling Innovative Technologies for Global Healthcare, EMBC 2020}},
isbn = {{9781728119908}},
issn = {{1557-170X}},
language = {{eng}},
pages = {{5494--5497}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS}},
title = {{Decision Tree Learning Algorithm for Classifying Knee Injury Status Using Return-to-Activity Criteria}},
url = {{http://dx.doi.org/10.1109/EMBC44109.2020.9176010}},
doi = {{10.1109/EMBC44109.2020.9176010}},
volume = {{2020-July}},
year = {{2020}},
}