# Emerging Technologies for Injury Identification in Sports Settings: A Systematic Review
## 4 Discussion  
This systematic review aimed to identify and evaluate emerging technologies for injury detection in active sports settings. The review highlighted twenty-one studies in this context, which identified an array of field-applicable tools and tests. With heterogeneity in results, it is difficult to compare and suggest the most appropriate tool(s) to be utilised across various sporting contexts. Biomarker-based solutions, NfL, GFAP, CSF markers, and salivary microRNAs, showed high diagnostic accuracy for SRC and PPCS by detecting specific neurological changes [11; 14; 16; 19; 21; 23; 25]. NfL and GFAP demonstrated greater sensitivity and correlation with injury severity. While these markers offer precise insights into subclinical injuries, their requirement for laboratory analysis leads to a delay in diagnosis delivery, which significantly limits their applicability to sideline utilisation, where point-of-care diagnosis is often needed. Relying on medical professionals with specialised equipment and their accompanying sophisticated results interpretation often deters a more widespread adoption of these tools [35; 36]. The limited frequent use of biomarkers for direct injury detection in sports settings stems from several key challenges. A lack of specificity in results, as found with creatine kinase or other inflammatory cytokines, can be elevated due to normal exercise-induced muscle damage or general physiological stress, rather than a specific injury occurrence, making it difficult to differentiate between physiological adaptation and a muscular injury [29; 30]. The inter-individual variability in biomarker levels, specifically in athletes, who all have unique confounding variables such as nutrition, hydration, and training status, complicates the \"norm\" ranges, as highlighted in [PERSON]'s study using inflammatory biomarker S100B [25; 29]. The current body of literature necessitates a universally accepted range of athlete-specific biomarkers. The practicality of such an approach poses significant logistical and resource challenges for many sports organisations [35; 36; 37]. Sensor-based technologies such as iMGs and HIT systems offer promising avenues for real-time injury event monitoring in contact sports [13; 15; 20]. However, further standardisation is required to ensure unbiased comparability and widespread adoption. Advanced imaging techniques, including 7T DKI, QSM, Rs-fMRI, and ultrasound, provided detailed insights into microstructural changes associated with SRC, PPCS, HIS, and OCD [9; 10; 11; 12; 15]. These methods are limited in field applicability, similar to biomarkers, requiring clinical settings with trained professionals. Recent studies have investigated field-based ultrasound tools for the assessment of HSI [38], which could be a middle ground between field-based applications and medical-level insights, but further investigation is required. The current review highlighted a considerable reduction in the number of participants (23.72%) who were retained for the duration of the included studies, which may limit the generalisability and reliability of findings. Additionally, gender imbalance was highly prevalent (95.14% male, 4.86% female *reported) and may affect implications for practitioners who work in female sports. Female athletes have different physiologies, different prevalences of injuries, for example, with HSI and knee ligament injuries [39]. This lack of developed female athlete research, specifically injury surveillance, was highlighted by a recent systematic review by [PERSON] [40] in 2024. A recent review clarified [41] that female athlete injury pathophysiology and biomechanical properties have a different make-up and thus require specific academic research. The underrepresentation of female athletes in these studies is notable and warrants further investigation, which may further help develop robust methodologies such as AI, giving broader scope to injury mechanisms and broader datasets.