Gastroenterology

Deep learning predicts onset acceleration of 38 age-associated diseases from blood and body composition biomarkers in the UK Biobank

A major challenge in multimorbid aging is understanding how diseases co-occur and identifying high-risk groups for accelerated disease development, but to date associations in the relative onset acceleration of disease diagnoses have not been used to characterise disease patterns. This study presents the development and evaluation of a neural network Cox model for predicting onset acceleration ris...

Large language models for extracting histopathologic diagnoses of colorectal cancer and dysplasia from electronic health records

Accurate data resources are essential for impactful medical research, but available structured datasets are often incomplete or inaccurate. Recent advances in open-weight large language models (LLMs) enable more accurate data extraction from unstructured text in electronic health records (EHRs) but have not yet been thoroughly validated for challenging diagnoses such as inflammatory bowel disease ...

Persistent Homology and Gabor Features Reveal Inconsistencies Between Widely Used Colorectal Cancer Training and Testing Datasets

Recent work on computer vision and image processing has relied substantially on open datasets, which allow for an objective comparison of techniques a...

Peritoneal Metastasis Prediction in Gastric Cancer: A Machine Learning Approach

Evaluate the predictive efficacy of six machine learning (ML) algorithms in identifying peritoneal metastasis in gastric cancer (GC) patients. Data fr...

Early Prediction of Anti-PD-1 Therapy Response in Hepatocellular Carcinoma Using Gut Microbiota Biomarkers

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, and response rates to anti-PD-1 therapy are suboptimal. Previous m...

Conversational AI Agent for Precision Oncology: AI-HOPE-WNT Integrates Clinical and Genomic Data to Investigate WNT Pathway Dysregulation in Colorectal Cancer

The WNT signaling pathway plays a critical role in colorectal cancer (CRC) initiation and progression, particularly in early-onset cases among underse...

Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability

Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that places a substantial burden on healthcare systems w...

From Mutation to Prognosis: AI-HOPE-PI3K Enables Artificial Intelligence-Agent Driven Integration of PI3K Pathway Data in Colorectal Cancer Precision Medicine

The incidence of early-onset colorectal cancer (EOCRC) is rising rapidly, with disproportionate health burdens falling on populations that experience ...

Data-Driven Predictive Modeling for Massive Intraoperative Blood Loss during Liver Transplantation: Integrating Machine Learning Techniques

Massive intraoperative bleeding (IBL) in liver transplantation (LT) poses serious risks and strains healthcare resources necessitating better predicti...

Decoding the JAK-STAT axis in colorectal cancer with AI-HOPE-JAK-STAT: A conversational artificial intelligence approach to clinical-genomic integration

The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammati...

Interpretable Machine Learning based Detection of Coeliac Disease

Coeliac disease, an autoimmune disorder affecting approximately 1% of the global population, is typically diagnosed on duodenal biopsy. However, inter...

Assessment and Prediction of Clinical Outcomes for ICU-Admitted Patients Diagnosed with Hepatitis: Integrating Sociodemographic and Comorbidity Data

Hepatitis, a disease characterized by inflammation of the liver, is a leading global health challenge that contributes to over 1.3 million deaths annu...

Artificial intelligence for precision oncology: AI-HOPE-MAPK uncovers clinically actionable MAPK alterations in colorectal cancer

The emergence of early-onset colorectal cancer (EOCRC), particularly among populations with disproportionate health burdens, has exposed critical gaps...

Using Large Language Models to Determine Reasons for Missed Colon Cancer Screening Follow-Up

Identifying reasons for missed preventive care, such as follow-up colonoscopy after an abnormal stool-based colon cancer screening test, is critical f...

Design and Implementation of an End-to-End AI-Driven Colonoscopy Recall Workflow at Scale

We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during a...

Multi-Omics and AI-/ML-Driven Integration of Nutrition and Metabolism in Cancer: A Systematic Review, Meta-Analysis, and Translational Algorithm

Cancer is increasingly recognized as a metabolic disease with strong nutritional determinants. Recent advances in multi-omics technologies and artific...

Artificial Intelligence in Gastrointestinal Endoscopy: A Comprehensive Systematic Review

Artificial intelligence (AI) has emerged as a transformative force in gastrointestinal (GI) endoscopy, offering enhancements in diagnostic accuracy, l...

From Rule-Based to DeepSeek R1 – A Robust Comparative Evaluation of Fifty Years of Natural Language Processing (NLP) Models To Identify Inflammatory Bowel Disease Cohorts

Natural language processing (NLP) can identify cohorts of patients with inflammatory bowel disease (IBD) from free text. However, limited sharing of c...

DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images

Spatial transcriptomics technology remains resource-intensive and unlikely to be routinely adopted for patient care soon. This hinders the development...

Development and International Validation of a Deep Learning Model for Predicting Acute Pancreatitis Severity from CT Scans

Acute pancreatitis (AP) is a common gastrointestinal disease with rising global incidence. While most cases are mild, severe AP (SAP) carries high mor...