ArticleLaboratory investigation; a journal of technical methods and pathology2020
Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.
Article in Laboratory investigation; a journal of technical methods and pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 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
- From traditional histopathology to virtual staining: deep learning approaches for assessing tissue lesions in farmed fish.Veterinary research communications · 2026Review
- Real-World Insights in Designing SteatoStat: An End-to-End Deep Learning Pipeline for Hepatic Steatosis Quantification.Diagnostics (Basel, Switzerland) · 2026Article
- Detection of macrovesicular steatosis in hematoxylin and eosin-stained histological images of human livers: A feature-based method.Journal of pathology informatics · 2026Article
- From prediction to practice: artificial intelligence as an enabling layer in liver transplant care - reflections on ILTS 2025.Frontiers in transplantation · 2026Article
- Toward efficient slide-level grading of liver biopsy via explainable deep learning framework.Medical & biological engineering & computing · 2025Article
- Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
- The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.Diagnostics (Basel, Switzerland) · 2024Review
- Digital pathology for nonalcoholic steatohepatitis assessment.Nature reviews. Gastroenterology & hepatology · 2024Review
- Laboratory variables-based artificial neural network models for predicting fatty liver disease: A retrospective study.Open medicine (Warsaw, Poland) · 2024Article
- Large-scale identification of undiagnosed hepatic steatosis using natural language processing.EClinicalMedicine · 2023Article
- Deep Learning-Based Image Analysis of Liver Steatosis in Mouse Models.The American journal of pathology · 2023Article
- Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Algorithmic transparency and interpretability measures improve radiologists' performance in BI-RADS 4 classification.European radiology · 2023Article
- Automated image analysis method to detect and quantify fat cell infiltration in hematoxylin and eosin stained human pancreas histology images.BBA advances · 2023Article
- Application of digital pathology and machine learning in the liver, kidney and lung diseases.Journal of pathology informatics · 2023Review
- A Novel Digital Algorithm for Identifying Liver Steatosis Using Smartphone-Captured Images.Transplantation direct · 2022Article
- [New advances of artificial intelligence in the diagnosis of non-alcoholic fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2022Review
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
- Deep learning-based image-analysis algorithm for classification and quantification of multiple histopathological lesions in rat liver.Journal of toxicologic pathology · 2022Article
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9 authors.
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Abstract
Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, pathology reviewing processes, when conducted manually, are subject to a high inter- and intra-reader variability, due to the overwhelmingly large number and significantly varying appearance of steatosis instances. This process is challenging as there is a large number of overlapped steatosis droplets with either missing or weak boundaries. In this study, we propose a deep-learning-based region-boundary integrated network for precise steatosis quantification with whole slide liver histopathology images. The proposed model consists of two sequential steps: a region extraction and a boundary prediction module for foreground regions and steatosis boundary prediction, followed by an integrated prediction map generation. Missing steatosis boundaries are next recovered from the predicted map and assembled from adjacent image patches to generate results for the whole slide histopathology image. The resulting steatosis measures both at the pixel level and steatosis object-level present strong correlation with pathologist annotations, radiology readouts and clinical data. In addition, the segregated steatosis object count is shown as a promising alternative measure to the traditional metrics at the pixel level. These results suggest a high potential of artificial intelligence-assisted technology to enhance liver disease decision support using whole slide images.
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