Use of artificial neural networks in improving renal transplantation outcomes

Nikolai Petrovsky, Soh Khum Tam, Vladimir Brusic, Graeme Russ, Luis Socha, Vladimir B. Bajic

Research output: Contribution to journalReview article

11 Citations (Scopus)

Abstract

Recent advances in renal transplantation, including the matching of major histocompatibility complex or new immunosuppressants, have improved 1-year survival of cadaver kidney grafts to more than 85%. Further optimization of kidney transplant outcomes is necessary to enhance both the graft survival time and the quality of life. Techniques derived from the artificial intelligence enable better prediction of graft outcomes by using donor and recipient data. The authors used an artificial neural network (ANN) to model kidney graft rejection and trained it with data on 1542 kidney transplants. The ANN correctly predicted 85% of successful and 72% of failed transplants. Also, ANN correctly predicted the type of rejection (hyperacute, acute, subacute, and chronic) for approximately 60% of the failed transplants. These results indicate that the ANN-based approach is useful for prediction of both the general outcomes of kidney transplants and the prediction of the type of rejection.

Original languageEnglish
Pages (from-to)6-13
Number of pages8
JournalGraft
Volume5
Issue number1
DOIs
Publication statusPublished - Jan 1 2002

ASJC Scopus subject areas

  • Transplantation

Fingerprint Dive into the research topics of 'Use of artificial neural networks in improving renal transplantation outcomes'. Together they form a unique fingerprint.

  • Cite this

    Petrovsky, N., Tam, S. K., Brusic, V., Russ, G., Socha, L., & Bajic, V. B. (2002). Use of artificial neural networks in improving renal transplantation outcomes. Graft, 5(1), 6-13. https://doi.org/10.1177/152216280200500101