Utilizing Machine Learning to Predict Liver Allograft Fibrosis by Leveraging Clinical and Imaging Data
Noninvasive approaches using ML for predicting significant GF perform well when considering demographic, clinical, and laboratory data; however, this performance is not enhanced with the use of US images.
Authors
- Madhumitha Rabindranath (ORCID: https://orcid.org/0000-0003-4820-1867)
- Mamatha Bhat (ORCID: https://orcid.org/0000-0003-1960-8449)
- Korosh Khalili (ORCID: https://orcid.org/0000-0002-3496-8744)
- Yingji Sun (ORCID: https://orcid.org/0009-0007-5238-5722)
Institutions
- University Health Network (CA)
- University of Toronto (CA)
- Toronto General Hospital (CA)
- Princess Margaret Cancer Centre (CA)
Publication Details
- Journal
- Clinical Transplantation
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1111/ctr.70148
- Citations
- 1
- Primary Topic
- Liver Disease Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35
Funders
- Mitacs