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

Journal: Clinical transplantation

PMID: 40245174

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).

Authors

Madhumitha Rabindranath

Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
Yingji Sun

Ajmera Transplant Centre, University Health Network, Toronto, Ontario, Canada.
Korosh Khalili

Department of Medical Imaging, University Health Network and University of Toronto, Toronto, Ontario, Canada.
Mamatha Bhat

Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.

Keywords

Adult Allografts Female Follow-Up Studies Graft Rejection Graft Survival Humans Liver Cirrhosis Liver Transplantation Machine Learning Male Middle Aged Neural Networks, Computer Postoperative Complications Prognosis Retrospective Studies Risk Factors Support Vector Machine Ultrasonography

External Resources

View on PubMed Access via DOI PubMed (40245174)

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

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals