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Artificial Intelligence Shows Promise in Concussion Management


TYPICALLY, WHEN WE TALK about analytics in sports, the discussion is focused on advanced player statistics – and more recently, on performance analytics through things like wearable sensor technologies and high-speed cameras. Advanced analytics and AI in sports are continuing to grow and evolve, even into the injury prevention and management arena. A recent research study I was involved in explored how machine learning analytics could be of use in modeling how high school sports concussion symptom(s) resolve.

While we know that concussion in sports is prevalent, clinicians, physicians and researchers also know that it's not a one-size-fits-all injury. Some of the most frequently asked questions when I'm evaluating and treating an athlete with concussion are: "How long before I can return to play?" and "Do you think I'll be ready for the next game/tournament?" Clinical management of concussions has come a long way. We're much more aggressive with symptom management and earlier physical activation, but timing of return-to-play protocols ultimately depends on the individual and the unique course of resolution his or her particular injury takes. The most accurate response to the common questions is, "We'll have to see how things go." That can be frustrating for patients, athletes, parents, trainers, coaches, teammates – and their physicians. So, insight into a clearer path for the anticipated resolution of concussion symptoms can be a huge benefit to patients and the sports medicine community as a whole. The purpose of this study was to implement a machine learning-based approach to model the estimated time for resolution of symptoms in high school athletes who suffered a concussion while playing sports.

We took three years of high school concussion data from high school athletes (mostly football players) who were diagnosed with concussion and examined the effectiveness of using an algorithm to predict their concussion symptom (headache, dizziness and difficulty with concentrating) resolution time. In effect, machine learning was being tested in its ability to predict how long concussion symptoms would take to resolve based on combinations of symptoms (and other variables) present in injured high school athletes. The main finding and importance are that machine learning and advanced analytics do have potential for clinical utility in managing concussion. Even with the limited data set available, computer modeling was able to accurately predict symptoms associated with prolonged recovery after a concussion.

So, what's the big deal here? (We already know that number and severity of presenting symptoms, as well as presence of certain modifying factors, are associated with prolonged recovery. A skilled and experienced physician with knowledge of existing research on concussion can evaluate a patient and make a reasonable estimate of severity as well as predicted recovery.) Well, there are significant clinical and research implications. This is an important first step. We believe the utilization of machine learning and AI is a modern approach that will become more and more accurate with the inclusion of more and more data. With time (and appropriate input from subject matter experts), computer analysis will be able to take into account the experiences of an unlimited number of concussed individuals with interdependent variables, complex and comprehensive data points at levels of complexity that would be impossible to achieve by a singular individual tasked with evaluating and managing that patient. Eventually, access to this kind of analysis will be available to the skilled physician (via an app or hand-held device), thereby dramatically improving the quality and accuracy of medical care relative to treatment approach, and predicted recovery. Also, machine learning with this kind of modeling and predictive analytics can accelerate traditional research by allowing division into groups that differ in the expected duration of symptoms and time to resolution – resulting in "cleaner" and more reliably certain outcomes. Traditional approaches to research would take years (even decades) to bear fruit.

The value of machine learning and big data analytics will be of increasing value in sports neurology, sports medicine, and all of medicine in general. From a practical application perspective, it can improve clinical management of injury and illness. From a research angle, machine learning can rapidly increase the efficiency of medical research. Once data is collected, scrubbed and organized, super-computing enables enhanced analysis, and modeling allows faster/cheaper research and testing. Importantly, the accuracy and utility of machine learning output will improve over time as more data is collected and added. Analytics, AI and big data are revolutionizing almost every aspect of sports. I'm excited to consider the future benefits to sports neurology.