Latest AI and machine learning research in gastroenterology for healthcare professionals.
Background & Aims: Intrahepatic biliary epithelial cell (BEC) heterogeneity remains challenging to define. Here, we sought to identify BEC subpopulations and biomarkers in mouse liver. Methods: We performed scRNA-seq on Sox9EGFP+ liver epithelium from mice subjected to bile duct ligation (BDL) and sham controls. A machine learning algorithm, NSForest, identified minimal, multi-gene signatures for ...
Existing medical imaging datasets for abdominal CT often lack three-dimensional annotations, multi-organ coverage, or precise lesion-to-organ associations, hindering robust representation learning and clinical applications. To address this gap, we introduce 3DLAND, a large-scale benchmark dataset comprising over 6,000 contrast-enhanced CT volumes with over 20,000 high-fidelity 3D lesion annotation...
Fanconi anemia (FA) is a rare genetic disorder of impaired DNA repair characterized by progressive bone marrow failure, congenital malformations, and ...
Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, largely due to the absence of reliable early stage biomarkers. Here, we ...
Background: Liver cancer primarily develops in patients with chronic liver disease (CLD), yet most cases are diagnosed at an advanced stage with poor ...
Ductular Reactions (DRs) are dynamic and complex multicellular responses that occur as a result of various hepatic injuries. Precise identification an...
Purpose: Translating foundation models into clinical practice requires evaluating their performance under compound distribution shift, where severe cl...
Liver fibrosis poses a substantial challenge in clinical practice, emphasizing the necessity for precise liver segmentation and accurate disease stagi...
Urinary bladder cancer surveillance requires tracking tumor sites across repeated interventions, yet the deformable and hollow bladder lacks stable la...
Purpose: Large language models (LLMs) are increasingly applied in radiology, but key challenges remain, including data leakage from cloud-based system...
Target discovery for IBD has traditionally relied on genetic associations, which lack the cellular resolution needed to identify novel, actionable, ce...
Randomized controlled trials estimate average treatment effects, but treatment response heterogeneity motivates personalized approaches. A critical qu...
Deep learning has the potential to improve colonoscopy by enabling 3D reconstruction of the colon, providing a comprehensive view of mucosal surfaces ...
The classification performance of deep neural networks relies strongly on access to large, accurately annotated datasets. In medical imaging, however,...
Liver tumour ablation presents a significant clinical challenge: whilst tumours are clearly visible on pre-operative MRI, they are often effectively i...
Digitizing large histopathology archives requires processing millions of scanned whole slide images that must be validated rapidly. Automated organ-of...
Early detection of colorectal cancer hinges on real-time, accurate polyp identification and resection. Yet current high-precision segmentation models ...
Accurate risk stratification of precancerous polyps during routine colonoscopy screenings is essential for lowering the risk of developing colorectal ...
Background: Gastrointestinal stromal tumor (GIST) is the most common gastrointestinal mesenchymal tumor, driven by tyrosine-protein kinase KIT and pla...
Oral microemulsions are one drug delivery system often implemented to improve intestinal permeability and oral bioavailability of poorly-water soluble...