This study, carried out by Tang and colleagues, investigates the accuracy of machine learning (ML) models in predicting major adverse cardiovascular event (MACE) risk using pericoronary adipose tissue (PCAT) in individuals with type 2 diabetes mellitus (T2DM). A retrospective approach was used, selecting 686 T2DM patients admitted to hospitals in Guangdong, China, between January 2017 and December 2021 as a cohort. Among the patients, 183 experienced MACE upon follow-up. All patients had initial imaging of their PCAT, and these images were fed to several ML models, along with other extenuating factors, to predict the probability of MACE occurrence, then compared the predictions to the actual patient outcomes to examine accuracy. The XGBoost model exhibited the most consistent and accurate performance, maintaining good discrimination and acceptable calibration in both the clinical and external models. Additionally, to ensure accurate evaluation of model performance, Brier scores were calculated for each model, with the XGBoost model scoring 0.248, indicating its value as an effective tool. Overall, the use of ML models in predicting MACE is critical, as past approaches such as logistic regression models prove inaccurate, failing to capture nonlinear interactions among variables.