Safeguarding Athletes Against Head Injuries Through Advances in Technology: A Scoping Review of the Uses of Machine Learning in the Management of Sports-Related Concussion

Abstract

Sports injury prevention is an important part of the athlete welfare and safeguarding research field. In sports injury prevention, sport-related concussion (SRC) has proved to be one of the most difficult and complex injuries to manage in terms of prevention, diagnosis, classification, treatment and rehabilitation. SRC can cause long-term health issues and is a commonly reported injury in both adult and youth athletes around the world. Despite increased knowledge of the prevalence of SRC, very few tools are available for diagnosing SRC in athletic settings. Recent technological innovations have resulted in different machine learning and deep learning methodologies being tested to improve the management of this complex sports injury. The purpose of this article is to summarize and map the existing research literature on the use of machine learning in the management of SRC, ascertain where there are gaps in the existing research and identify recommendations for future research. This is explored through a scoping review. A systematic search in the three electronic databases SPORTDiscus, PubMed and Scopus identified an initial 522 studies, of which 24 were included in the final review, the majority of which focused on machine learning for the prediction and prevention of SRC (N = 10), or machine learning for the diagnosis and classification of SRC (N = 11). Only 3 studies explored machine learning approaches for the treatment and rehabilitation of SRC. A main finding is that current research highlights promising practical uses (e.g., more accurate and rapid injury assessment or return-to-sport participation criteria) of machine learning in the management of SRC. The review also revealed a narrow research focus in the existing literature. As current research is primarily conducted on male adolescents or adults from team sports in North America there is an urgent need to include wider demographics in more diverse samples and sports contexts in the machine learning algorithms. If research datasets continue to be based on narrow samples of athletes, the development of any new diagnostic and predictive tools for SRC emerging from this research will be at risk. Today, these risks appear to mainly affect the health and safety of female athletes.

Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports

“…More recent efforts have employed feature-based machine learning, 146 where multiple response features in different anatomical regions are combined to predict the likelihood of injury. 21 , 160 They have been shown to improve injury prediction performance relative to other scalar values, such as peak kinematics or peak MPS.…”

Prevention of sports-related concussion in soccer: a comprehensive review of the literature

“…In a more recent online survey of 8104 youth soccer teams with 101 699 players aged 7–14 years old, the overall concussion incidence rate was 0.85/1000 athlete exposures (AEs). Games were 5.7 times more likely than practices to result in concussions (1.73/1000 AEs vs. 0.27/1000 AEs) 1 .…”

“…Globally, adult and adolescent athletes regularly report sports-related concussions (SRCs), which pose a safety concern at the competitive and recreational levels of contact and noncontact sports 1 . A concussion may result from a collision, blow, or shock to the head or body that causes the head and brain to move rapidly back and forth.…”

“…The effects of concussions on an athlete’s memory, reaction time, and balance are well established from a clinical standpoint. Furthermore, athletes with a history of concussions are more likely to sustain subsequent concussions, which can result in cognitive decline, depression, or early degenerative change, such as chronic traumatic encephalopathy 1 . This in-depth analysis summarizes the research on SRCs, including suggestions for avoiding future problems.…”