Gastroenterology

Deep learning-based intraluminal gas modeling for anatomically accurate synthetic CT in MRI-based radiation therapy.

Stochastic bowel and rectal gas complicates MRI/CT deformable image registration (DIR) for synthetic CT (sCT) generation, requiring manual corrections. We propose a DIR-free, two-stage deep learning framework to improve intraluminal gas definition in sCT, streamlining MRI-only simulation and enhancing dosimetric reliability. Methods: We developed a two-stage GAN framework for sCT generati...

Development and validation of a deep learning model for liver shear stiffness regression using abdominal multiparametric MRI across multiple sites and vendors.

OBJECTIVE: Liver stiffness measurement is important for assessing chronic liver disease (CLD). MR elastography (MRE) requires specialized hardware and expertise. Non-invasive deep learning (DL) models using multiparametric abdominal MRI may provide an accessible alternative. We sought to develop and validate a DL model for predicting continuous liver shear stiffness from non-contrast multiparametr...

Deep-Motion-Net: GNN-based volumetric liver shape reconstruction from single-view 2D projections.

PURPOSE: Internal anatomical motion challenges precise radiation delivery during external beam radiotherapy. Estimating and compensating for anatomica...

The Asian Pacific Association of the Study of the Liver expert survey on artificial intelligence-assisted reporting of liver histopathology in metabolic dysfunction associated fatty liver disease.

INTRODUCTION: Artificial intelligence (AI) and digital pathology have the potential to augment liver biopsy interpretation in MAFLD in clinical practi...

Multi-phase hybrid metabolomics framework identifies clinically applicable plasma signatures for early detection of gastric cancer.

Plasma metabolomics offers significant potential for non-invasive biomarker discovery in gastric cancer (GC), yet conventional analytical workflows fa...

Integrative analysis of the mouse cecal microbiome across diet, age, and weight in the diverse BXD population.

BACKGROUND: The gut microbiota adapts to and shapes the host's metabolic state through affecting circulating metabolites and consequent gene regulator...

Video-based assessment tool for workflow analysis in minimally invasive colorectal surgery: expert consensus-based development and multicentre validation of ColoWorkflow.

BACKGROUND: Minimally invasive colorectal surgery is characterized by significant procedural variability, a difficult learning curve, and complication...

An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer.

This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of ...

Thrombospondin-2 is a performant biomarker of at-risk MASH and advanced MASH fibrosis in a large multicentre European cohort.

BACKGROUND: Non-invasive biomarkers with biological rationale are needed for identifying patients with progressive metabolic dysfunction-associated st...

Shaping the future of postoperative recurrence in Crohn's disease: personalised approaches with AI-enabled imaging and multi-omics.

Postoperative recurrence (POR) is a major challenge in the long-term management of Crohn's disease (CD), affecting up to 70% of patients within the fi...

Predicting biliary complications after liver transplantation: a machine learning and nomogram-based exploratory study.

BACKGROUND: Accurate risk stratification of biliary complications (BCs) after liver transplantation (LT) remains challenging. This study aimed to deve...

A novel deep-learning approach for monitoring gastrointestinal air variation during radiotherapy in young patients using radiographs.

Objective.To develop deep-learning (DL) predictive models of internal gastrointestinal (GI) gas from radiographs, a surrogate for dose degradation in ...

Three-dimensional modeling of sensory nerve architecture and eosinophil and mast cell interactions in eosinophilic gastrointestinal disease.

Increased sensory nerve density has been described in type II inflammatory conditions and is linked to eosinophil and mast cell infiltration and neuro...

Deep Learning Framework for Early Detection of Pancreatic Cancer Using Multi-modal Medical Imaging Analysis.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal forms of cancer, with a five-year survival rate below 10% primarily due to late...

Interpreting MALDI imaging data for rare types of ampullary cancer using machine learning.

Rare tumor diseases are difficult to diagnose and there is a lack of routine diagnostic procedures. Approaches must be found that allow comprehensive ...

Establishment of prognostic prediction model based on lipid metabolism related genes in esophageal squamous cell carcinoma by machine learning algorithms.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related mortality worldwide, with a high prevalence and poor prognosi...

Application of machine-learning algorithms to identify the key determinants of risk for HIV, hepatitis C and hepatitis B in primary care settings.

BACKGROUND: Testing for Blood-Borne-Viruses (BBVs) such as the human immunodeficiency virus (HIV), hepatitis C virus (HCV) and hepatitis B virus (HBV)...

WBT-DC pipeline: a cross-cohort and cross-platform disease classification pipeline based on whole-blood transcriptomics.

BACKGROUND: Machine-learning models based on tissue transcriptomic data are powerful tools for disease classification. However, their clinical adoptio...

Identifying potential relationships between air pollutants and neutrophil extracellular traps formation in metabolic dysfunction-associated fatty liver disease by integrating computational toxicology and multi-omics data.

Metabolic dysfunction-associated fatty liver disease (MASLD) is a highly prevalent liver condition with a complex etiology increasingly linked to air ...

Prediction of Acute Liver Injury Trajectory in Patients Following Acetaminophen Overdose: A Multibiomarker Machine Learning Proof-of-Concept Study.

Clinical translation of novel therapies can be hindered by heterogeneity-driven sample size inflation in late-stage trials. In acetaminophen-induced l...