Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Abstract

Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.

Bile Composition as a Diagnostic and Prognostic Tool in Liver Transplantation

“…An international registry of machine-perfused LTs and routine collection and analysis of bile and perfusate samples may be used to apply machine learning/ artificial intelligence to help with the validation of viability criteria. (54) Newer techniques, such as proteomics or microRNA (miRNA) analysis of bile, offer a new diagnostic window for future machine perfusion studies.…”

Machine learning in liver transplantation: a tool for some unsolved questions?

“…These models have consequently been widely applied in many fields of Gastroenterology and Hepatology to facilitate clinicians’ diagnostic or therapeutic algorithms, or predict patient outcomes [3,4]. Examples of applications of ML in Hepatology include: predicting fibrosis in patients with viral hepatitis or nonalcoholic fatty liver disease; ascertaining the presence of esophageal varices in patients with cirrhosis; establishing the prognosis for patients with end‐stage liver disease [3,5]. Certain aspects of solid organ transplantation, such as allocation, post‐transplant outcome, and the management of immunosuppression, have also been explored using ML‐based models [6–11].…”

Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor

“…Recently, many reports have described the development of AI models for the detection and diagnosis of liver tumors; some studies have aimed to predict outcomes after treatments, which may be applicable for the personalized management of patients ( 18 , 19 ).…”