A machine learning model for accurate prediction of CMV end-organ disease

ML models-particularly ensemble-based approaches-demonstrate high diagnostic accuracy in identifying CMV EOD and may offer valuable support for early risk stratification and clinical decision-making. Integration of these tools into routine practice could improve outcomes through timely, individualized care strategies in vulnerable transplant populations.