Evidence map›Paper›PMID 33855647›Full record

ReviewJournal of experimental orthopaedics2021

Machine learning methods in sport injury prediction and prevention: a systematic review.

Hans Van Eetvelde, Luciana D Mendonça, Christophe Ley, Romain Seil, Thomas Tischer

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of experimental orthopaedics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

110 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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50 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hans Van EetveldeDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281-S9, 9000, Ghent, Belgium. Hans.VanEetvelde@UGent.be.ORCID http://orcid.org/0000-0003-1421-7721
Luciana D MendonçaGraduate Program in Rehabilitation and Functional Performance, Universidade Federal Dos Vales Do Jequitinhonha E Mucuri (UFVJM), Diamantina, Minas Gerais, Brazil.ORCID http://orcid.org/0000-0002-4495-1807
Christophe LeyDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281-S9, 9000, Ghent, Belgium.ORCID http://orcid.org/0000-0002-2290-8437
Romain SeilDepartment of Orthopaedic Surgery, Centre Hospitalier Luxembourg and Luxembourg Institute of Health, Luxembourg, Luxembourg.ORCID http://orcid.org/0000-0001-8806-9384
Thomas TischerDepartment of Orthopaedic Surgery, University of Rostock, Rostock, Germany.ORCID http://orcid.org/0000-0002-3942-0235

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeInjuries are common in sports and can have significant physical, psychological and financial consequences. Machine learning (ML) methods could be used to improve injury prediction and allow proper approaches to injury prevention. The aim of our study was therefore to perform a systematic review of ML methods in sport injury prediction and prevention.

methodsA search of the PubMed database was performed on March 24th 2020. Eligible articles included original studies investigating the role of ML for sport injury prediction and prevention. Two independent reviewers screened articles, assessed eligibility, risk of bias and extracted data. Methodological quality and risk of bias were determined by the Newcastle-Ottawa Scale. Study quality was evaluated using the GRADE working group methodology.

resultsEleven out of 249 studies met inclusion/exclusion criteria. Different ML methods were used (tree-based ensemble methods (n = 9), Support Vector Machines (n = 4), Artificial Neural Networks (n = 2)). The classification methods were facilitated by preprocessing steps (n = 5) and optimized using over- and undersampling methods (n = 6), hyperparameter tuning (n = 4), feature selection (n = 3) and dimensionality reduction (n = 1). Injury predictive performance ranged from poor (Accuracy = 52%, AUC = 0.52) to strong (AUC = 0.87, f1-score = 85%).

conclusionsCurrent ML methods can be used to identify athletes at high injury risk and be helpful to detect the most important injury risk factors. Methodological quality of the analyses was sufficient in general, but could be further improved. More effort should be put in the interpretation of the ML models.

Indexed as

Injury predictionInjury preventionMachine LearningSport injury

Identifiers

PMID33855647
PMCPMC8046881

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.