AI used to predict the toll of concussions on student athletes over time

An American football player holds the ball during a training session of the Rio Football Academy team inside the luxury condominium Peninsula, located at the Barra da Tijuca neighborhood in Rio de Janeiro, Brazil, on January 24, 2024 - Copyright AFP MAURO PIMENTEL Researchers at the University of Michigan have deployed artificial intelligence to predict the health consequences that sport-related concussions might have on student athletes over the course of their college athletic careers. The study found that five different AI models outperformed a simple benchmark model when predicting the progression of possible health ramifications from concussions, and revealed multiple surprising findings about concussion management. "To our knowledge, this is the first research that leverages AI to predict the change in key clinical outcomes for a concussed athlete after college play is over," states lead researcher Lauren Czerniak. Czerniak adds: "This is an important topic as there is a growing concern about the effects of sport-related concussions after college play is over, and there is a lot of uncertainty around when the effects of sport-related concussions begin to present clinically." For this study, Czerniak and colleagues wanted to know if AI could predict three clinical outcomes: The impact on overall quality of life (symptom burden). Cognitive status. The risk of psychological distress. They also wanted to know which AI models predicted those outcomes well, and what factors--such as frequency and intensity of concussion and the sports played (sport exposure)--influenced the progression of those outcomes over an athlete's career. The researchers used baseline and exit data from about 3,200 NCAA athletes in the Concussion, Assessment, Research, and Education Consortium, a national concussion research network of NCAA athletes and U.S. military service academy cadets across 30 different institutions. In addition to the CARE Consortium data, researchers fed the models with demographic as well as health information collected during the athlete's college career. The key findings were: The last two findings were deemed to be especially significant: that the frequency and intensity of the number of concussions had little to no impact. "When initially designing the study, our research team hypothesized that the frequency and intensity of concussions would play the biggest role in an athlete's progression over their collegiate career," Czerniak describes. "AI allowed us to tease out the important predictors and demonstrate that our hypothesis was incorrect." Concussion is one of the most complex injuries in sports medicine, and many high school and college athletes experience one or many concussions while playing. Among the NCAA athletes at CARE, it's estimated that around 10% have experienced one to several sport-related concussions. There is a growing interest in understanding the long-term effects of concussion and head impact exposure, with a particular focus on athletes. Czerniak says future studies could entail larger and more diverse populations. Given enough data is available, there is potential to embed AI into a software application that uses an athlete's or patient's clinical record to predict areas of progression so that proactive measures or monitoring could be taken. The study appears in the journal Annals of Biomedical Engineering, titled "Prediction of Symptom Burden, Cognitive Status, and Risk of Psychological Distress in NCAA Athletes with Sport-Related Concussion(s): Findings from the NCAA-DoD CARE Consortium."