Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. To guarantee that female athletes receive the optimal care they deserve, researchers must employ advanced neuroimaging techniques and sophisticated machine-learning models. These tools enable an in-depth investigation of the underlying mechanisms responsible for concussion symptoms stemming from neuronal dysfunction in female athletes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions.

The National Football League is hoping that artificial intelligence can help reduce concussions in athletes. The NFL "Digital Athlete" is an artificial intelligence tool that uses TV images and sensors embedded in helmets, mouth guards and shoulder pads to try to reduce injuries. The tool creates a digital replica of an NFL athlete in a virtual environment. Using machine learning and computer vision technology, the tool then pinpoints impacts and injuries and helps researchers find new ways to improve player safety. "Having the computers understand how many times a player hits his helmet during the course of a game [helps] find ways to reduce the amount of helmet contact,"," Jeff Miller, NFL executive vice president, told New Scientist. Within the environment generated by the tool, an infinite number of game scenarios can be run, "giving the ability to test out new safety equipment, test out rule changes and predict player injury events and recovery trajectories eventually", says Dr Priya Ponnapalli, principal scientist at Amazon Machine Learning Solutions Lab. "What we've shown, I would say pretty definitively, is the relationship between years of play and risk of the disease," said Jesse Mez, Associate Professor of Neurology at Boston University. Mez hopes that the NFL will make more data available for study, "right now no helmet sensor data [is] made available to any investigators at universities.