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Figure 4. Overview of machine learning applications in donor liver assessment (2021-2024). (A) Distribution of studies across subcategories (biliary tract segmentation, graft weight, liver segmentation, liver volume, steatosis), with steatosis being the most studied area; (B) Algorithm types used by category (linear, neural networks, non-linear, probabilistic, tree-based methods), with neural networks dominating due to increased use of images; (C) Performance metrics comparison across categories using multiple measures (Accuracy, AUC, Dice coefficient, MAE); and (D) Distribution of data types utilized (histology, liver photos/videos, radiology, tabular), demonstrating heavy reliance on histological and radiological data, particularly for steatosis assessment. AUC: Area under the curve; MAE: mean absolute error.