Machine learning analysis of contrast-enhanced ultrasound (CEUS) for the diagnosis of acute graft dysfunction in kidney transplant recipients.
Journal:
Medical ultrasonography
PMID:
39231287
Abstract
AIM: The aim of the study was to develop machine learning algorithms (MLA) for diagnosing acute graft dysfunction (AGD) in kidney transplant recipients based on contrast-enhanced ultrasound (CEUS) analysis of the graft.Materials and methods: This prospective study involved 71 patients with kidney transplant undergoing CEUS during follow-up. AGD wasdefined as an increase in serum creatinine levels of at least 25% compared to the baseline of the last three months. The control group consisted of patients with stable kidney graft function (SGF). The top five CEUS parameters that achieved the best discrimination between the AGD and SGF groups were selected based on ANOVA testing and then employed as input for training MLA (naïve Bayes (NB), k-nearest neighbors (k-NN), and logistic regression (LR)). The models were validated by leave-one-out cross-validation.