Gastroenterology

Machine Learning Approach Enables Highly Accurate Identification of At-Risk Metabolic Dysfunction-Associated Steatohepatitis.

AIM: At-risk metabolic dysfunction-associated steatohepatitis (MASH), characterized by significant activity and fibrosis, increases the risk of liver complications. Liver stiffness measurement (LSM), commonly used to detect significant fibrosis, has limitations in terms of accessibility and performance in certain populations. We aimed to develop a simple-to-use machine learning (ML) model to ident...

Unraveling gene interaction networks in colorectal cancer and inflammatory bowel disease via a novel hybrid radial basis function network.

HMC-transducer: hierarchical mamba-CNN transducer for robust liver tumor segmentation.

Accurate segmentation of liver tumors from computed tomography (CT) scans is critical for clinical diagnosis and treatment planning, yet it remains a ...

Serum peptidomics by MALDI-TOF MS coupled with machine learning approaches for diagnosis of primary liver cancer.

Primary liver cancer (PLC) is one of the most common malignant tumors worldwide. Due to its insidious onset, 70 to 80% of patients are diagnosed at an...

Diagnostic Performance of Preoperative Imaging-based Radiomics Models for Predicting Liver Metastases in Colorectal Cancer: A Systematic Review and Meta-analysis.

RATIONALE AND OBJECTIVES: To evaluate the diagnostic performance of preoperative computed tomography (CT) and magnetic resonance imaging (MRI)-based r...

Deep learning-derived CT body composition enhances survival risk stratification beyond the TNM system in locally advanced gastric cancer: a multi-modality cohort study.

BACKGROUND: Survival outcomes in locally advanced gastric cancer remain heterogeneous despite standard treatment and outcome classifications. Visceral...

Full-Endoscopic Spine Surgery for Adjacent Segment Disease Using Artificial Intelligence-Assisted 3-Dimensional MRI/CT Fusion Imaging: A Case Report.

CASE: An 82-year-old woman presented with right medial thigh pain. Her medical history included multiple lumbar decompression and fusion surgeries. Sh...

Spatial transcriptional profiling of CHB liver biopsies reveals an undetected population of zonally biased HBV-integrated cells.

BACKGROUND & AIMS: HBV covalently closed circular DNA (cccDNA) and HBV-integrated DNA (iDNA) are features of chronic HBV (CHB). Elimination of both ce...

Clinical and methodological considerations in artificial intelligence-assisted endoscopic ultrasonography for pancreatic mass detection.

Incidence and implications of abstinence-induced recompensation in alcohol-related cirrhosis.

BACKGROUND & AIMS: Alcohol abstinence enables hepatic recompensation in patients with decompensated alcohol-related cirrhosis. This study investigated...

Machine learning-enabled quantification of hepatocellular necrosis in the liver after lethal Marburg and Ebola virus exposures.

The liver is a key target of pathogenic orthomarburgviruses and orthoebolaviruses, with injury common in severe filovirid disease, yet high-resolution...

Explainable Artificial Intelligence for the Mayo Endoscopic Score in Ulcerative Colitis.

INTRODUCTION: The Mayo endoscopic score (MES) is used widely in ulcerative colitis (UC) for severity assessment and therapeutic decision-making. Deep ...

Machine Learning-Based Detection of Intestinal Metaplasia Identifies Clinically Missed Cases.

CONTEXT.—: Gastric intestinal metaplasia is a recognized precursor and risk factor to gastric cancer, and correct diagnosis is required for clinical d...

A deep learning radiopathomic signature predicts recurrence risk of hepatocellular carcinoma after hepatectomy.

Accurate prediction of recurrence risk after hepatectomy still remains a clinical challenge for hepatocellular carcinoma (HCC). We develop a deep lear...

Deep learning-based automated contrast enema analysis to improve the assessment of Hirschsprung disease.

OBJECTIVES: To compare the radiologic assessment of Hirschsprung disease (HD) based on contrast enema with automated image analysis using a deep neura...

A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer.

Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the ...

AI-driven optimization of sustainable solvent-based extraction: A deep learning approach for green sample preparation.

BACKGROUND: This study introduces a data-driven deep learning framework for optimizing the extraction of plant biomolecules, aiming to improve both ef...

Patient-specific deep offline artificial pancreas for blood glucose regulation in type 1 diabetes.

Due to insufficient insulin secretion, patients with type 1 diabetes mellitus (T1DM) are prone to blood glucose fluctuations ranging from hypoglycemia...

Development of a diagnostic model for esophageal achalasia assessed by esophageal high-resolution manometry using artificial intelligence.

Esophageal high-resolution manometry (HRM) is an important tool for diagnosing and assessing esophageal achalasia. Artificial intelligence (AI)-assist...

An integrative multiomics random forest framework for robust biomarker discovery.

BACKGROUND: High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic ...