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Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning. / Roldán Ciudad, Elisa; Reeves, Neil D.; Cooper, Glen et al.
In: Computer Methods in Biomechanics and Biomedical Engineering, 30.08.2025, p. 1-15.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Roldán Ciudad, E, Reeves, ND, Cooper, G & Andrews, K 2025, 'Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning', Computer Methods in Biomechanics and Biomedical Engineering, pp. 1-15. https://doi.org/10.1080/10255842.2025.2551846

APA

Roldán Ciudad, E., Reeves, N. D., Cooper, G., & Andrews, K. (2025). Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning. Computer Methods in Biomechanics and Biomedical Engineering, 1-15. Advance online publication. https://doi.org/10.1080/10255842.2025.2551846

Vancouver

Roldán Ciudad E, Reeves ND, Cooper G, Andrews K. Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning. Computer Methods in Biomechanics and Biomedical Engineering. 2025 Aug 30;1-15. Epub 2025 Aug 30. doi: 10.1080/10255842.2025.2551846

Author

Roldán Ciudad, Elisa ; Reeves, Neil D. ; Cooper, Glen et al. / Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning. In: Computer Methods in Biomechanics and Biomedical Engineering. 2025 ; pp. 1-15.

Bibtex

@article{5d67d74fd438481183e19edecbf014e0,
title = "Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning",
abstract = "Anterior cruciate ligament (ACL) reconstruction rates are rising, particularly among female athletes, though causes remain unclear. This study: (i) identify accurate machine learning models to predict ACL length, strain, and force during six high-impact and daily activities; (ii) assess the significance of kinematic and constitutional parameters; and (iii) analyse gender-based injury risk patterns. Using 9,375 observations per variable, 42 models were trained. Cubist, Generalized Boosted Models (GBM), and Random Forest (RF) achieved the best R2, RMSE, and MAE. Knee flexion and external rotation strongly predicted ACL strain and force. Female athletes showed higher rotation during cuts, elevating ACL strain and risk.",
author = "{Rold{\'a}n Ciudad}, Elisa and Reeves, {Neil D.} and Glen Cooper and Kirstie Andrews",
year = "2025",
month = aug,
day = "30",
doi = "10.1080/10255842.2025.2551846",
language = "English",
pages = "1--15",
journal = "Computer Methods in Biomechanics and Biomedical Engineering",
issn = "1025-5842",
publisher = "Taylor & Francis",

}

RIS

TY - JOUR

T1 - Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning

AU - Roldán Ciudad, Elisa

AU - Reeves, Neil D.

AU - Cooper, Glen

AU - Andrews, Kirstie

PY - 2025/8/30

Y1 - 2025/8/30

N2 - Anterior cruciate ligament (ACL) reconstruction rates are rising, particularly among female athletes, though causes remain unclear. This study: (i) identify accurate machine learning models to predict ACL length, strain, and force during six high-impact and daily activities; (ii) assess the significance of kinematic and constitutional parameters; and (iii) analyse gender-based injury risk patterns. Using 9,375 observations per variable, 42 models were trained. Cubist, Generalized Boosted Models (GBM), and Random Forest (RF) achieved the best R2, RMSE, and MAE. Knee flexion and external rotation strongly predicted ACL strain and force. Female athletes showed higher rotation during cuts, elevating ACL strain and risk.

AB - Anterior cruciate ligament (ACL) reconstruction rates are rising, particularly among female athletes, though causes remain unclear. This study: (i) identify accurate machine learning models to predict ACL length, strain, and force during six high-impact and daily activities; (ii) assess the significance of kinematic and constitutional parameters; and (iii) analyse gender-based injury risk patterns. Using 9,375 observations per variable, 42 models were trained. Cubist, Generalized Boosted Models (GBM), and Random Forest (RF) achieved the best R2, RMSE, and MAE. Knee flexion and external rotation strongly predicted ACL strain and force. Female athletes showed higher rotation during cuts, elevating ACL strain and risk.

U2 - 10.1080/10255842.2025.2551846

DO - 10.1080/10255842.2025.2551846

M3 - Journal article

SP - 1

EP - 15

JO - Computer Methods in Biomechanics and Biomedical Engineering

JF - Computer Methods in Biomechanics and Biomedical Engineering

SN - 1025-5842

ER -