Accuracy of Foundation Artificial Intelligence Models for Hepatic Macrovesicular Steatosis Quantification in Frozen Sections

Context.—: Accurate intraoperative assessment of macrovesicular steatosis in donor liver biopsies is critical for transplant decisions but is often limited by interobserver variability and freezing artifacts that can obscure histologic details. Artificial intelligence (AI) offers a potential solution for standardized and reproducible evaluation. Objective.—: To evaluate the diagnostic performance of 2 self-supervised learning (SSL)-based foundation models, Prov-GigaPath and UNI, for classifying macrovesicular steatosis on frozen liver biopsy sections, compared with assessments by surgical pathologists. Design.—: This retrospective study included 131 frozen liver biopsy specimens from 68 donors collected between November 2022 and September 2024. Slides were digitized into whole slide images, tiled into patches, and used to extract embeddings with Prov-GigaPath and UNI; slide-level classifiers were then trained and tested. Intraoperative diagnoses by on-call surgical pathologists were compared with ground truth determined from independent reviews of permanent sections by 2 liver pathologists. Accuracy was evaluated for both a 5-category classification and a clinically significant binary threshold (