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Artificial intelligence, machine learning, and deep learning in liver transplantation.

Mamatha Bhat1, Madhumitha Rabindranath2, Beatriz Sordi Chara3

  • 1Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.

Journal of Hepatology
|May 19, 2023
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can improve liver transplantation (LT) by offering data-driven insights for complex patient management. AI applications range from optimizing pre-transplant decisions to predicting outcomes and identifying risks post-transplant.

Keywords:
liver graftsurvivaltransplantationwaitlist mortality

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Transplantation Medicine

Background:

  • Liver transplantation (LT) is a critical treatment for end-stage liver disease, but recipient management is complex.
  • Current clinical decision-making relies on integrating diverse data (demographic, clinical, lab, imaging, omics), often involving subjectivity.
  • Artificial intelligence (AI) offers a data-driven approach to enhance the precision and objectivity of LT management.

Purpose of the Study:

  • To explore the potential applications of AI, including machine learning and deep learning, in liver transplantation.
  • To highlight how AI can address the complexities of managing liver transplant recipients.
  • To identify specific pre- and post-transplant scenarios where AI can provide significant benefits.

Main Methods:

  • Review of existing and potential AI applications in liver transplantation.
  • Discussion of machine learning and deep learning methodologies relevant to LT data.
  • Analysis of AI's role in optimizing decision-making processes.

Main Results:

  • AI can optimize pre-transplant processes like candidacy assessment and donor-recipient matching, reducing waitlist mortality.
  • Post-transplant, AI can predict patient and graft survival and identify risks for disease recurrence and complications.
  • AI tools show potential for personalized clinical decision-making in LT.

Conclusions:

  • AI offers a powerful, data-driven approach to enhance liver transplantation management.
  • AI applications can improve outcomes by personalizing treatment plans and predicting complications.
  • Addressing challenges like data imbalance and privacy is crucial for widespread AI adoption in LT.