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Related Concept Videos

Tissue Transplantation01:24

Tissue Transplantation

354
Tissue transplantation is a significant medical procedure involving the transfer of cells, tissues, or organs from a donor to a recipient, with the primary aim of restoring lost functions. This procedure is crucial in treating a broad spectrum of diseases, including kidney diseases, liver failure, heart disease, and certain types of cancers.
The Biology of Tissue Transplantation
The biology of tissue transplantation hinges on the Major Histocompatibility Complex (MHC) molecules. These molecules...
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Bone marrow transplant is a potential cure for several diseases, including cancer and specific genetic disorders. Notably, this procedure is applicable for patients suffering from aplastic anemia, certain types of leukemia, severe combined immunodeficiency disease (SCID), Hodgkin's disease, non-Hodgkin's lymphoma, multiple myeloma, thalassemia, sickle-cell disease, and certain cancers.
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The transformative potential of artificial intelligence in solid organ transplantation.

Mouhamad Al Moussawy1, Zoe S Lakkis2, Zuhayr A Ansari2

  • 1Department of Surgery, Thomas E. Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States.

Frontiers in Transplantation
|July 12, 2024
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Summary

Artificial intelligence (AI) offers solutions for solid organ transplantation challenges, including organ shortages and complications. This review details AI

Keywords:
artificial intelligencemachine learningmonitoringpre-evaluationprognosticationrejection diagnosistransplantation

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Area of Science:

  • Medical Informatics
  • Transplant Surgery
  • Artificial Intelligence in Medicine

Background:

  • Solid organ transplantation faces significant hurdles, including limited donor organ availability and post-transplant complications.
  • Current strategies require enhancement to improve patient outcomes and graft survival.

Purpose of the Study:

  • To review the current applications of artificial intelligence (AI) in addressing challenges within solid organ transplantation.
  • To highlight the role of machine learning in pretransplant evaluation, rejection prediction, and post-operative care.

Main Methods:

  • Literature review of recent advancements in AI for solid organ transplantation.
  • Analysis of machine learning algorithms applied to transplant-related data.

Main Results:

  • AI demonstrates potential in improving pretransplant organ matching and recipient selection.
  • Machine learning models show promise in predicting transplant rejection and patient outcomes.
  • AI facilitates personalized medicine approaches in transplant care.

Conclusions:

  • Artificial intelligence is revolutionizing solid organ transplantation by addressing key challenges.
  • AI integration enhances diagnostic accuracy, predictive capabilities, and personalized treatment strategies.
  • This review underscores the transformative impact of AI on the future of transplant medicine.