Improving Liver Transplant Success: Leveraging AI and Machine Learning to Predict Graft Failure

Abstract

Objective: Organ transplants are crucial interventions that can save patients whose livers are in the final stages of deterioration. However, physicians face challenges in predicting which transplanted organs may fail after the procedure. This study aims to explore how artificial intelligence (AI) and machine learning (ML) algorithms can predict the failure of liver transplant grafts and their potential to enhance transplant outcomes.

Methods: The failure rates and their ability to predict photosensitive transplant outcomes. The systematic review involved examining peer-reviewed articles published between January 2018 and August 2024. The researchers utilized the PubMed and IEEE Xplore databases, along with Scopus, to conduct their search. The review included studies that demonstrated AI and ML methods for forecasting graft failure, along with assessments of liver transplant outcomes and advancements in post-transplant care.

Results: The ability to predict liver transplant graft failure demonstrates significant effectiveness using deep learning algorithms that are part of machine learning methods. These algorithms integrate patient data with evaluations of liver quality and immunological markers, enabling them to provide precise long-term predictions for transplants. With the successful use of AI models, healthcare providers can identify patients at greater risk of rejection, allowing them to create tailored immunosuppressive therapies to avert graft failure.

Conclusion:  Techniques in Artificial Intelligence and Machine Learning have become essential for forecasting the outcomes of liver transplant graft failures. Leveraging these technologies contributes to more personalized medical treatment for transplant patients, ultimately improving the long-term success rates of transplants. Further studies are required to refine predictive algorithms, develop real-time patient information systems, and address clinical issues related to data privacy and biases in algorithms.