Concussion is a form of brain injury caused by a sudden bump, blow, or violent shake to the head. It is difficult to diagnose as it rarely looks the same twice. Some athletes feel sick and dizzy; others do not. Some lose consciousness, yet around 90% do not<\/a>. <\/p>\n Many of the sports we play and watch carry a significant risk of head impact and brain injury. Such injuries might occur from a single heavy impact: a mistimed tackle, a punch, a fall. Others add up over many years of frequent knocks, leaving athletes at an increased risk of Parkinson\u2019s disease, Alzheimer\u2019s or other forms of dementia. <\/p>\n Our own research has shown how even repeated impacts that don\u2019t cause concussion in rugby<\/a>, football<\/a> and boxing<\/a> can quietly damage the brain\u2019s blood supply and function over time. <\/p>\n The more precisely we can pinpoint which parts of the brain have been affected by an impact, the better we can tailor an athlete\u2019s recovery. Rather than applying a one size fits all checklist for returning to play, doctors and sports staff can instead use data-backed insights to personalise rehabilitation plans. In doing so, they should track not just the player\u2019s physical recovery, but also their psychological readiness to return to action. <\/p>\n AI could also help with one of the thorniest issues in concussion management: the pressure placed on athletes to return too soon by clubs, coaches and themselves. An independent AI model, drawing on a range of data from brain scans and blood tests to surveys of an athlete\u2019s mood, could provide medical staff with a solid foundation of objective evidence to resist such pressures. <\/p>\n \n We are already exploring these approaches, particularly in objective blood and saliva testing, in collaborative research<\/a> with the charity Head for Change. The organisation supports former athletes living with neurodegenerative conditions to better understand long-term brain health in contact sport. <\/p>\n AI can also turn data from wearable sensors in helmets and gumshields, for example, into maps of the brain\u2019s injuries<\/a>. This matters because every athlete is different. Factors like neck strength, fatigue and previous injury history mean that a single hit inflicts varying damage in different people. <\/p>\n Like any tool, AI is not without its risks. It can sometimes present false claims as definite facts. If scientists rely on these summaries, they may build studies on flawed foundations. There is also the danger of false reassurance, where a tool incorrectly labels an injury as low risk for concussion and an athlete is returned to play too soon. <\/p>\n Ethical hurdles involve both old and new data. AI is only as reliable as its training material, resulting in observed gender and racial biases<\/a>. If models use historical data from, say, solely male professionals, they may fail women, children or amateur players. We also face questions regarding athletes\u2019 medical data, and whether it is owned by the player, club, or even the insurer. <\/p>\nHow does this help athletes?<\/h2>\n
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\n How AI can predict rugby injuries before they happen<\/a>
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