Rouzrokh P, Ramazanian T, Wyles CC, et al. - This study was sought to demonstrate the potential of a convolutional neural network (CNN) model to assess the risk of hip dislocation based on postoperative anteroposterior (AP) pelvis radiographs. Researchers retrospectively examined radiographs for a cohort of 13,970 primary total hip arthroplasties (THA) with 374 dislocations over 5 years of follow-up. It has been reported that the existing prediction methods fail to distinguish individuals at high risk of dislocation following THA. According to the findings, the radiographic classifier model has high sensitivity and negative predictive value and can be combined with clinical risk factor information for rapid evaluation of risk for dislocation following THA. The findings suggested that the model further implies radiographic locations which may be important in understanding the etiology of prosthesis dislocation. In orthopedics, the model is an illustration of the potential of automated imaging AI models.