Live-Donor Kidney Transplant Outcome Prediction (L-TOP) using artificial intelligence.

Journal: Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
PMID: 38684469

Abstract

BACKGROUND: Outcome prediction for live-donor kidney transplantation improves clinical and patient decisions and donor selection. However, the currently used models are of limited discriminative or calibration power and there is a critical need to improve the selection process. We aimed to assess the value of various artificial intelligence (AI) algorithms to improve the risk stratification index.

Authors

  • Hatem Ali
    From the University Hospitals of Coventry and Warwickshire, United Kingdom.
  • Mahmoud Mohammed
    Department of Internal Medicine and Nephrology, University Hospitals of Mississippi, Mississippi, USA.
  • Miklos Z Molnar
    Division of Nephrology, Department of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States; Department of Transplantation and Surgery, Semmelweis University, Budapest, Hungary; Methodist University Hospital Transplant Institute, Memphis, TN, United States; Department of Surgery, University of Tennessee Health Science Center, Memphis, TN, United States.
  • Tibor Fülöp
    Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi. Electronic address: tiborfulop.nephro@gmail.com.
  • Bernard Burke
    Research Centre for Health and Life Sciences, Coventry University, Coventry, United Kingdom.
  • Sunil Shroff
    Xtend.AI, Medindia.net, MOHAN Foundation.
  • Arun Shroff
    Xtend.AI, Medindia.net, MOHAN Foundation.
  • David Briggs
    Histocompatibility and Immunogenetics NHS Blood and Transplant, Birmingham, United Kingdom.
  • Nithya Krishnan
    From the University Hospitals of Coventry and Warwickshire, United Kingdom.

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