ReviewCureus2023
An Overview of Machine Learning Applications in Sports Injury Prediction.
Review in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in ACL injury prediction and prevention: a systematic review.Journal of orthopaedic surgery and research · 2026Pooled it
- Application of Artificial Intelligence for Predicting Sports Injuries and Customizing Personalized Prevention Strategies: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Sports Medicine: A Decision-Centered Framework for the Future Sports Physician.Diagnostics (Basel, Switzerland) · 2026Review
- [Progress and application of machine learning in sports injury research].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- AI-Based Electromyographic Analysis of Single-Leg Landing for Injury Risk Prediction in Taekwondo Athletes.Healthcare (Basel, Switzerland) · 2026Article
- A multimodal deep learning-based model for posture asymmetry recognition and sports injury risk prediction in adolescent table tennis athletes.Frontiers in physiology · 2026Article
- Machine learning approaches for enhanced estimation of reference evapotranspiration (ETo): a comparative evaluation.Scientific reports · 2025Article
- Sports injury risk prediction based on temporal graph encoding and graph neural networks: A cross-sport transfer learning framework.Scientific reports · 2025Article
- Intelligent optimization of track and field teaching using machine learning and wearable sensors.Scientific reports · 2025Article
- A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes.Sports medicine (Auckland, N.Z.) · 2025Article
- Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.BMC cardiovascular disorders · 2025Article
- Predicting Sprint Potential: A Machine Learning Model Based on Blood Metabolite Profiles in Young Male Athletes.European journal of sport science · 2025Article
- Exploring the Relations Between Running Variability and Injury Susceptibility: A Scoping Review.Sports (Basel, Switzerland) · 2025Review
- A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes.medRxiv : the preprint server for health sciences · 2025Article
- Review
- Diagnostic Applications of AI in Sports: A Comprehensive Review of Injury Risk Prediction Methods.Diagnostics (Basel, Switzerland) · 2024Review
- Microwave Digital Twin Prototype for Shoulder Injury Detection.Sensors (Basel, Switzerland) · 2024Article
- Gear Classification in Skating Cross-Country Skiing Using Inertial Sensors and Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- [Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Predictive model for identifying mild cognitive impairment in patients with type 2 diabetes mellitus: A CHAID decision tree analysis.Brain and behavior · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.