Inflammatory Bowel Disease

Epigenetic patient stratification reveals a sub-endotype of type 2 asthma with altered B-cell response

Despite biomarker-guided treatment strategies, clinical outcomes among patients with type 2 (T2)-high asthma remain heterogeneous, with some patients responding poorly to T2-targeted biologic therapies. We developed a contrastive machine learning method for patient stratification based on whole-blood DNA methylation (DNAm), applying it to pediatric asthma cohorts of Latino (discovery; n=1,016) and...

Glomerular Segmentation, Classification, and Pathomic Feature-based Prediction of Clinical Outcomes in Minimal Change Disease and Focal Segmental Glomerulosclerosis

Conventional assessment of Focal Segmental Glomerulosclerosis and Minimal Change Disease focuses on the presence/extent of segmental (SS) and global (GS) glomerulosclerosis. While SS and GS represent ongoing and terminal process, encoded in non-SS/GS glomeruli is prognostic information that can be extracted before structural changes are visually discernable. This study applies computational image ...

Entropy-Guided Sample-Specific Feature Selection for Robust Incomplete Multi-Omics Learning in Gut Microbiome Disease Prediction and Biomarker Discovery

The rapid advances in multi-omics data integration technologies have opened unprecedented avenues for dissecting the mechanisms and accelerating the c...

Interpretable machine learning applied to high-dimensional salivary proteomics accurately classifies pediatric inflammatory bowel diseases

Inflammatory bowel diseases (IBD), including Crohn’s disease (CD), ulcerative colitis (UC), and IBD-unclassified (IBD-U), are chronic inflammatory dis...

Artificial Intelligence Significantly Improves Adenoma Detection Rate but Does Not Affect Polyp Detection Rate in Colonoscopy: A Propensity Score Matching Study

Colorectal cancer (CRC) remains a major cause of cancer-related morbidity and mortality worldwide. Endoscopy and adenoma removal are effective in redu...

Evaluating Large Language Models for Colonoscopy Preparation Assistance: Correctness and Diversity in Synthetic Dialogues

Colorectal cancer is the third leading cause of cancer-related deaths in the United States, and colonoscopy remains the gold standard for early detect...

Non-invasive epidermal proteomics and machine learning permits molecular subclassification of psoriasis and eczematous dermatitis

Current approaches to selecting molecularly targeted therapies (biologics and oral small molecules) for immune-mediated skin diseases largely overlook...

Mechanosensitive TRPV4 immunohistochemistry improves deep learning-based grading of ductal carcinoma in situ beyond H&E morphology

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer spanning a biologic continuum from atypical ductal hyperplasia (ADH) to high-grade les...

Human Phenotype Ontology (HPO) Mapper: Semantic Mapping of Clinical Findings to the Human Phenotype Ontology Using AI-Powered Embeddings and LLM-Based Quality Control

Structured phenotypic annotations linked to genetic data can drive diagnostic insight and therapeutic discovery in complex diseases. However, poor res...

Use of artificial intelligence to measure colorectal polyp size without a reference object.

BACKGROUND AND STUDY AIMS: Polyp size is crucial for determining colonoscopy surveillance intervals. We present an artificial intelligence (AI) model ...

Exploring shared pathogenic mechanisms and biomarkers in hepatic fibrosis and inflammatory bowel disease through bioinformatics and machine learning.

BACKGROUND: The coexistence of hepatic fibrosis (HF) and inflammatory bowel disease (IBD) represents a significant clinical concern due to their poorl...

MNet-SAt: A Multiscale Network with Spatial-enhanced Attention for Segmentation of Polyps in Colonoscopy

Objective: To develop a novel deep learning framework for the automated segmentation of colonic polyps in colonoscopy images, overcoming the limitat...

SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data

Colorectal cancer (CRC) remains a leading cause of cancer-related deaths worldwide, with polyp removal being an effective early screening method. Ho...

SAM-Mamba: Mamba Guided SAM Architecture for Generalized Zero-Shot Polyp Segmentation

Polyp segmentation in colonoscopy is crucial for detecting colorectal cancer. However, it is challenging due to variations in the structure, color, ...

Diagnosis and Severity Assessment of Ulcerative Colitis using Self Supervised Learning

Ulcerative Colitis (UC) is an incurable inflammatory bowel disease that leads to ulcers along the large intestine and rectum. The increase in the pr...

Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation

Accurate medical image segmentation is essential for effective diagnosis and treatment planning but is often challenged by domain shifts caused by v...

AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD.

Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel...

[Effect of an artificial intelligence-assisted recognition system on colonoscopy quality].

To explore the value of the artificial intelligence (AI)-assisted recognition system in the detection quality of colonoscopy. From January 2023, the...

Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records

Colonoscopy screening effectively identifies and removes polyps before they progress to colorectal cancer (CRC), but current follow-up guidelines re...

Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series

Anomaly detection in clinical time-series holds significant potential in identifying suspicious patterns in different biological parameters. In this...