Details

Autor(en) / Beteiligte

• Khorsandi, Shirin Elizabeth
• Hardgrave, Hailey J.
• Osborn, Tamara
• Klutts, Garrett
• Nigh, Joe
• Spencer-Cole, Richard T.
• Kakos, Christos D.
• Anastasiou, Ioannis
• Mavros, Michail N.
• Giorgakis, Emmanouil

Titel

Artificial Intelligence in Liver Transplantation

Ist Teil von

• Transplantation proceedings, 2021-12, Vol.53 (10), p.2939-2944

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• •Artificial intelligence includes computer systems to emulate human intelligence.•Machine learning models are self-adaptive models with the ability to improve predictions.•Machine learning and deep learning models can optimize liver transplant utilization and equitability.•Liver transplant applications include allocation, matching, survival prediction, transplant oncology. Advancements based on artificial intelligence have emerged in all areas of medicine. Many decisions in organ transplantation can now potentially be addressed in a more precise manner with the aid of artificial intelligence. All elements of liver transplantation consist of a set of input variables and a set of output variables. Artificial intelligence identifies relationships between the input variables; that is, how they select the data groups to train patterns and how they can predict the potential outcomes of the output variables. The most widely used classifiers to address the different aspects of liver transplantation are artificial neural networks, decision tree classifiers, random forest, and naïve Bayes classification models. Artificial intelligence applications are being evaluated in liver transplantation, especially in organ allocation, donor-recipient matching, survival prediction analysis, and transplant oncology. In the years to come, deep learning–based models will be used by liver transplant experts to support their decisions, especially in areas where securing equitability in the transplant process needs to be optimized.