Cargando…

Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes

BACKGROUND: Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction...

Descripción completa

Detalles Bibliográficos
Autores principales: Jauhiainen, Susanne, Kauppi, Jukka-Pekka, Krosshaug, Tron, Bahr, Roald, Bartsch, Julia, Äyrämö, Sami
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9442771/
https://www.ncbi.nlm.nih.gov/pubmed/35984748
http://dx.doi.org/10.1177/03635465221112095
_version_ 1784782895488958464
author Jauhiainen, Susanne
Kauppi, Jukka-Pekka
Krosshaug, Tron
Bahr, Roald
Bartsch, Julia
Äyrämö, Sami
author_facet Jauhiainen, Susanne
Kauppi, Jukka-Pekka
Krosshaug, Tron
Bahr, Roald
Bartsch, Julia
Äyrämö, Sami
author_sort Jauhiainen, Susanne
collection PubMed
description BACKGROUND: Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction performance. PURPOSE: To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: The authors used 3-dimensional motion analysis and physical data collected from 791 female elite handball and soccer players. Four common classifiers were used to predict ACL injuries (n = 60). Area under the receiver operating characteristic curve (AUC-ROC) averaged across 100 cross-validation runs (mean AUC-ROC) was used as a performance metric. Results were confirmed with repeated permutation tests (paired Wilcoxon signed-rank-test; P < .05). Additionally, the effect of the most common class imbalance handling techniques was evaluated. RESULTS: For the best classifier (linear support vector machine), the mean AUC-ROC was 0.63. Regardless of the classifier, the results were significantly better than chance, confirming the predictive ability of the data and methods used. AUC-ROC values varied substantially across repetitions and methods (0.51-0.69). Class imbalance handling did not improve the results. CONCLUSION: The authors’ approach and data showed statistically significant predictive ability, indicating that there exists information in this prospective data set that may be valuable for understanding injury causation. However, the predictive ability remained low from the perspective of clinical assessment, suggesting that included variables cannot be used for ACL prediction in practice.
format Online
Article
Text
id pubmed-9442771
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-94427712022-09-06 Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes Jauhiainen, Susanne Kauppi, Jukka-Pekka Krosshaug, Tron Bahr, Roald Bartsch, Julia Äyrämö, Sami Am J Sports Med Articles BACKGROUND: Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction performance. PURPOSE: To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: The authors used 3-dimensional motion analysis and physical data collected from 791 female elite handball and soccer players. Four common classifiers were used to predict ACL injuries (n = 60). Area under the receiver operating characteristic curve (AUC-ROC) averaged across 100 cross-validation runs (mean AUC-ROC) was used as a performance metric. Results were confirmed with repeated permutation tests (paired Wilcoxon signed-rank-test; P < .05). Additionally, the effect of the most common class imbalance handling techniques was evaluated. RESULTS: For the best classifier (linear support vector machine), the mean AUC-ROC was 0.63. Regardless of the classifier, the results were significantly better than chance, confirming the predictive ability of the data and methods used. AUC-ROC values varied substantially across repetitions and methods (0.51-0.69). Class imbalance handling did not improve the results. CONCLUSION: The authors’ approach and data showed statistically significant predictive ability, indicating that there exists information in this prospective data set that may be valuable for understanding injury causation. However, the predictive ability remained low from the perspective of clinical assessment, suggesting that included variables cannot be used for ACL prediction in practice. SAGE Publications 2022-08-19 2022-09 /pmc/articles/PMC9442771/ /pubmed/35984748 http://dx.doi.org/10.1177/03635465221112095 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Articles
Jauhiainen, Susanne
Kauppi, Jukka-Pekka
Krosshaug, Tron
Bahr, Roald
Bartsch, Julia
Äyrämö, Sami
Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
title Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
title_full Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
title_fullStr Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
title_full_unstemmed Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
title_short Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes
title_sort predicting acl injury using machine learning on data from an extensive screening test battery of 880 female elite athletes
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9442771/
https://www.ncbi.nlm.nih.gov/pubmed/35984748
http://dx.doi.org/10.1177/03635465221112095
work_keys_str_mv AT jauhiainensusanne predictingaclinjuryusingmachinelearningondatafromanextensivescreeningtestbatteryof880femaleeliteathletes
AT kauppijukkapekka predictingaclinjuryusingmachinelearningondatafromanextensivescreeningtestbatteryof880femaleeliteathletes
AT krosshaugtron predictingaclinjuryusingmachinelearningondatafromanextensivescreeningtestbatteryof880femaleeliteathletes
AT bahrroald predictingaclinjuryusingmachinelearningondatafromanextensivescreeningtestbatteryof880femaleeliteathletes
AT bartschjulia predictingaclinjuryusingmachinelearningondatafromanextensivescreeningtestbatteryof880femaleeliteathletes
AT ayramosami predictingaclinjuryusingmachinelearningondatafromanextensivescreeningtestbatteryof880femaleeliteathletes