AI-Assisted Liver Transplant Outcome Prediction

Liver transplantation is a critical, life-saving procedure for individuals with advanced liver disease or acute liver failure, but the outcomes can vary greatly. Accurate prediction of these outcomes is essential for optimizing organ allocation and enhancing patient management. Traditional methods, which rely on clinical scoring systems, often fall short due to subjective evaluations and their limited ability to process large-scale data. This study introduces an AI-driven approach to predict liver transplant outcomes using machine learning algorithms. The model was developed and validated with a comprehensive dataset of liver transplant cases, including donor and recipient characteristics, laboratory results, and clinical parameters. Advanced AI techniques, such as Multi-layer Perceptron (MLP) and survival analysis through the Kaplan-Meier estimator, were employed to identify complex patterns and interactions within the data. The AI model significantly outperformed traditional scoring systems, achieving an AUCROC of 93.11% for 1-year post-transplant survival, thus enabling effective identification of high-risk patients and providing personalized risk assessments. By incorporating diverse pre-transplant clinical data, the model captures the multifactorial nature of liver transplant outcomes, offering insights that go beyond traditional methods. Its potential to transform clinical workflows includes facilitating data-driven decisions in organ allocation and supporting personalized care strategies. This innovation highlights the potential of AI to refine liver transplant predictions, optimize patient outcomes, and advance organ allocation policies.