XIBI: AI for the prevention of ACL injury

We developed an artificial intelligence that calculates the predisposition of an athlete to suffer an anterior cruciate ligament (ACL) injury, in order to prevent it or help in its rehabilitation. These account for 10% of injuries in high-performance college athletes and are 4 to 6 times higher in female athletes compared to men playing the same sports. An injury of this type has multiple physical, psychological and economic implications for the athlete and his family and there is currently no quantitative method that contributes to its prevention. We proposed an artificial intelligence that tracks the athlete's joints to biomechanically analyze his performance in the execution of a jump and calculate the dynamic valgus index (DVI). Subsequently, a multiple regression model calculates the probability of injury based on variables such as DVI, age, sex, previous injuries and level of physical activity. The expected result is a Python software with a user-friendly graphical user interface, which requests as input data the variables needed for the regression and displays as output data an estimate of the probability of ACL injury. For our project, we had access to test subjects to train our model and to allow us to corroborate results.

With this project, our team was nominated for Conexion Tec, in the area of research and improvement proposal, where we won the second national place.