Artificial intelligence in liver transplantation: Opportunities and challenges

Figure 1 Artificial intelligence nomenclature. ANN: Artificial neural network.

Figure 2 Types of machine learning model. XGBoost: eXtreme Gradient Boosting.

Figure 3 Application of artificial intelligence in deceased donor liver transplantation. HCC: Hepatocellular carcinoma; ANN: Artificial neural network; OPOM: Optimised prediction of mortality; SVM: Support vector machines; AVM-SIL: Support vector machine-single instance learning; XGBoost: eXtreme Gradient Boosting; LT: Liver transplantation.

Figure 4 Application of artificial intelligence in living donor liver transplantation. SVM-POLY: Support Vector Machine-Polynomial kernel; SVM-LINEA: Support Vector Machine Linear; U-Net: Convolutional neural network architecture, named after distinctive U shape; ANN: Artificial neural network; SVM: Support vector machine; XGBoost: eXtreme Gradient Boosting; LDLT: Living donor liver transplantation.

Figure 5 Radar plot for prediction of 90 days mortality in cirrhosis. AUROC: Area under the receiver operating characteristic curve; DNN: Deep neural networks; LR: Logistic regression.

Figure 6 Accuracy and interpretability of various machine learning models. XGBoost: eXtreme Gradient Boosting.