ArticleEBioMedicine2020
Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Assessment of the Diagnostic Performance and Clinical Impact of AI in Hepatic Steatosis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Deep-learning-Assisted Photoacoustic and Ultrasound Evaluation for Pre-transplant Human Liver Graft Quality and Transplant Suitability.medRxiv : the preprint server for health sciences · 2026Article
- From prediction to practice: artificial intelligence as an enabling layer in liver transplant care - reflections on ILTS 2025.Frontiers in transplantation · 2026Article
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Article
- Antibody selection and automated quantification of TRPV1 immunofluorescence on human skin.Scientific reports · 2024Article
- Artificial Intelligence Advances in Transplant Pathology.Bioengineering (Basel, Switzerland) · 2023Review
- Deep Learning-Based Image Analysis of Liver Steatosis in Mouse Models.The American journal of pathology · 2023Article
- Diagnostic Assessment of Deep Learning Algorithms for Frozen Tissue Section Analysis in Women with Breast Cancer.Cancer research and treatment · 2023Article
- Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology.Clinical and molecular hepatology · 2022Review
- A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images.Transplantation direct · 2022Article
- Application of machine learning in liver transplantation: a review.Hepatology international · 2022Review
- Artificial intelligence in surgery: A research team perspective.Current problems in surgery · 2022Article
- Liver transplantation in the era of non-alcoholic fatty liver disease/metabolic (dysfunction) associated fatty liver disease: the dilemma of the steatotic liver graft on transplantation and recipient survival.Hepatobiliary surgery and nutrition · 2022Article
- Review
- A Clinical Tool to Guide Selection and Utilization of Marginal Donor Livers With Graft Steatosis in Liver Transplantation.Transplantation direct · 2022Article
- "Big Data" Approaches for Prevention of the Metabolic Syndrome.Frontiers in genetics · 2022Review
- Liver Transplantation With Grafts From Super Obese Donors.Transplantation direct · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundPathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies.
methodsWe developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model.
findingsThe model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set).
interpretationUse of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation.
fundingMid-America Transplant Society.
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Registered trials
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