fig3

Figure 3. Overview of machine learning applications in pre-liver transplant risk assessment (2021-2024). (A) Distribution of studies across subcategories (acetaminophen toxicity, cardiac assessment, cirrhosis, fibrosis, primary biliary cholangitis); (B) Algorithm types used by category (clustering, linear methods, neural networks), with neural networks showing predominance particularly in image-based studies; (C) Performance metrics comparison across categories using accuracy and AUC; and (D) Distribution of data types utilized (ECG, histology, radiology, tabular), highlighting the increasing adoption of imaging-based approaches. AUC: Area under the curve; ECG: electrocardiogram.