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

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.