ArticleAmerican journal of physiology. Gastrointestinal and liver physiology2024
BiliQML: a supervised machine-learning model to quantify biliary forms from digitized whole slide liver histopathological images.
Article in American journal of physiology. Gastrointestinal and liver physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 4 citations in OpenAlex.
- Cholangiocyte biology in primary sclerosing cholangitis and other cholangiopathies: pathogenesis, clinical insights, and experimental tools.Physiological reviews · 2026Review
- Benefits and challenges to therapeutic targeting of bile acid circulation in cholestatic liver disease.Hepatology (Baltimore, Md.) · 2025Review
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
The progress of research focused on cholangiocytes and the biliary tree during development and following injury is hindered by limited available quantitative methodologies. Current techniques include two-dimensional standard histological cell-counting approaches, which are rapidly performed, error prone, and lack architectural context or three-dimensional analysis of the biliary tree in opacified livers, which introduce technical issues along with minimal quantitation. The present study aims to fill these quantitative gaps with a supervised machine-learning model (BiliQML) able to quantify biliary forms in the liver of anti-keratin 19 antibody-stained whole slide images. Training utilized 5,019 researcher-labeled biliary forms, which following feature selection, and algorithm optimization, generated an
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.