Application of artificial intelligence and machine learning for risk stratification acute kidney injury among hematopoietic stem cell transplantation patients: PCRRT ICONIC AI Initiative Group Meeting Proceedings.

Journal: Clinical nephrology
PMID: 39545392

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.

Authors

  • Rupesh Raina
    Akron Nephrology Associates/Cleveland Clinic Akron General Medical Center, Akron, OH, USA. rraina@akronchildrens.org.
  • Kush Doshi
  • Pushan Aggarwal
  • Parker Kim
  • Jonathan Sasse
  • Sidharth Sethi
  • Carolyn Abitbol
    Department of Pediatrics, Division of Pediatric Nephrology, University of Miami Miller School of Medicine/Holtz Children's Hospital, Miami, FL, USA.
  • Rolla Abu-Arja
  • Kianoush Kashani
    Division of Nephrology and Hypertension.

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