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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Transplant. Mar 18, 2026; 16(1): 114000
Published online Mar 18, 2026. doi: 10.5500/wjt.v16.i1.114000
Published online Mar 18, 2026. doi: 10.5500/wjt.v16.i1.114000
Application of machine learning in the research progress of post-kidney transplant rejection
Yun-Peng Guo, Tongliao Clinical Medical College, Inner Mongolia Medical University, Tongliao 028000, Inner Mongolia Autonomous Region, China
Quan Wen, Bo Chen, Department of Urinary Surgery, Tongliao People's Hospital, Tongliao 028000, Inner Mongolia Autonomous Region, China
Yu-Yang Wang, The Graduate School, Inner Mongolia Medical University, Huhehot 010000, Inner Mongolia Autonomous Region, China
Gai Hang, Department of Urinary Surgery, Tongliao City Hospital, Tongliao 028000, Inner Mongolia Autonomous Region, China
Co-first authors: Yun-Peng Guo and Quan Wen.
Author contributions: Guo YP was responsible for drafting of manuscript; Guo YP and Wen Q were responsible for study concept and design, translation of the manuscript as co-first authors; Wang YY and Hang G were responsible for performed the research; Chen B was responsible for critical revision of the manuscript; all authors have read and approved the final manuscript.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bo Chen, MD, PhD, Chief Physician, Professor, Department of Urinary Surgery, Tongliao People's Hospital, No. 668 Horqin Street, Horqin District, Tongliao 028000, Inner Mongolia Autonomous Region, China. chenmuxin@126.com
Received: September 9, 2025
Revised: October 8, 2025
Accepted: December 23, 2025
Published online: March 18, 2026
Processing time: 127 Days and 17.7 Hours
Revised: October 8, 2025
Accepted: December 23, 2025
Published online: March 18, 2026
Processing time: 127 Days and 17.7 Hours
Core Tip
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.