Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data.

ABSTRACT:

Background and aim

Liver transplant (LT) recipients may succumb to graft-related pathologies, contributing to graft fibrosis (GF). Current methods to diagnose GF are limited, ranging from procedural-related complications to low accuracy. With recent advances in machine learning (ML), we aimed to develop a noninvasive tool using demographic, clinical, laboratory, and B-mode ultrasound (US) features to predict significant fibrosis (METAVIR≥F2).

Methods

We used a nested 10-fold cross-validation approach with grid-search for hyperparameter fine-tuning to train an artificial neural network (ANN) and a support vector machine (SVM) to classify mild fibrosis (F0-F1) and significant fibrosis (F2-F4) on 1131 patients. We calculated Shapley values to identify top-ranked features, deter

SUBMITTER: Rabindranath M 

PROVIDER: S-EPMC12005582 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature