Application of machine learning in the research progress of post-kidney transplant rejection

Abstract

Post-kidney transplant rejection is a critical factor influencing transplant success rates and the survival of transplanted organs. With the rapid advancement of artificial intelligence technologies, machine learning (ML) has emerged as a powerful data analysis tool, widely applied in the prediction, diagnosis, and mechanistic study of kidney transplant rejection. This mini-review systematically summarizes the recent applications of ML techniques in post-kidney transplant rejection, covering areas such as the construction of predictive models, identification of biomarkers, analysis of pathological images, assessment of immune cell infiltration, and formulation of personalized treatment strategies. By integrating multi-omics data and clinical information, ML has significantly enhanced the accuracy of early rejection diagnosis and the capability for prognostic evaluation, driving the development of precision medicine in the field of kidney transplantation. Furthermore, this article discusses the challenges faced in existing research and potential future directions, providing a theoretical basis and technical references for related studies.

Keywords: Machine learning; Kidney transplant; Rejection; Predictive models; Biomarkers; Pathological image analysis; Immune cell infiltration; Precision medicine

Core Tip: Recent advances in machine learning (ML) have opened new avenues for the early prediction and precise diagnosis of rejection in kidney transplantation. ML techniques can analyze large, complex datasets to identify patterns and correlations that may not be readily apparent through conventional analytical methods. By leveraging diverse data sources, including clinical, laboratory, and imaging data, ML models can provide more accurate risk assessments and facilitate timely interventions to mitigate the risk of rejection.