Abstract
Acute kidney injury (AKI) is a frequent, severe complication of hematopoietic stem cell transplantation (HSCT) and is associated with an increased risk of morbidity and mortality. Recent advances in artificial intelligence (AI) and machine learning (ML) have showcased their proficiency in predicting AKI, projecting disease progression, and accurately identifying underlying etiologies. This review examines the central aspects of AKI post-HSCT, veno-occlusive disease (VOD) in HSCT recipients, discusses present-day applications of artificial intelligence in AKI, and introduces a proposed ML framework for the early detection of AKI risk.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
