This study by Samad et al. explores the use of machine learning to predict deterioration in ventricular function in patients with repaired Tetralogy of Fallot (rTOF). The researchers selected 153 patients who had undergone two cardiac MRI scans at least six months apart. Using clinical, electrocardiogram, and imaging data from the first scan, they trained a support vector machine (SVM) to predict future deterioration in ventricular size and function. The model categorized patients into three groups: no deterioration, minor deterioration, or major deterioration. The SVM model achieved a high area under the curve (AUC) of 0.87 when distinguishing major deterioration from no deterioration and 0.82 when distinguishing any deterioration from no deterioration. Key predictors included left ventricular ejection fraction, pulmonary regurgitation fraction, LV circumferential strain, and the patient's age at surgical repair. The study demonstrates that machine learning can enhance prediction accuracy, potentially leading to more personalized patient monitoring.