Kunze KN, Polce EM, Nwachukwu BU, et al. - This study was sought to construct and confirm a machine-learning algorithm to prognosticate clinically significant functional improvements after hip arthroscopy for femoroacetabular impingement syndrome and to (2) develop a digital application capable of providing patients with individual risk profiles to determine their propensity to gain clinically significant improvements in function. Between January 2012-2017, researchers conducted a retrospective review of consecutive hip arthroscopy patients that had undergone cam/pincer correction, labral preservation, and capsular closure from one large academic and three community hospitals operated on by a single high-volume hip arthroscopist. The study included a sum of 818 patients with a median (interquartile range) age of 32.0 (22.0 – 42.0) and 69.2% female, of which 74.3% achieved the minimal clinically important difference for the HOS-ADL. The results of this study demonstrate that the stochastic boosting gradient model conferred excellent predictive ability for propensity to gain clinically significant improvements in function after hip arthroscopy. According to the findings, an open-access digital application was developed, which may augment shared decision-making and allow for preoperative risk stratification. External validation of this model is needed to validate the performance of these algorithms as the generalizability is currently unknown.