GERD

Overview of State-of-the-Art Learning-Based Classification Methods in Medical Imaging.

Learning-based image classification has become central to modern medical imaging, but the field is changing rapidly: foundation models, vision-language models (VLMs), and label-efficient pretraining are reshaping which methods are clinically useful. This review focuses on the state of the art rather than re-explaining well-established models. We summarize learning paradigms, contrast classical mac...

Shared molecular signatures linking gastroesophageal reflux disease and major depressive disorder revealed by integrated machine learning.

BACKGROUND: Gastroesophageal reflux disease (GERD) and major depressive disorder (MDD) frequently co-occur, yet their shared molecular mechanisms remain poorly understood. This study aimed to identify a robust gene signature linking GERD and MDD and to evaluate its clinical relevance. METHODS: Transcriptomic data from five GEO datasets were analyzed, including three GERD datasets (52 GERD samples ...

Quantification of mitral regurgitation: from traditional methods to artificial intelligence.

Mitral regurgitation (MR) is a common valvular disorder and associated with adverse outcomes. Echocardiography is the primary imaging modality for MR ...

GatedGeoGO:Multi-Modal Geometry-Aware Network with Gated Fusion and GO Semantic Attention for Protein Function Prediction.

Proteins play essential roles in diverse biological processes, and accurate function annotation is fundamental for understanding cellular mechanisms a...

Deep Learning-Based Multiclass Classification of Mitral Valve Etiologies Using Limited B-Mode and Color Doppler Echocardiography: Internal and External Validation.

BACKGROUND: Accurate etiologic classification of the mitral valve (MV) is essential for guiding clinical management but remains dependent on expert vi...

Protein language models are accidental taxonomists.

Protein-protein interactions (PPIs) are fundamental to nearly all biological processes, yet their experimental characterization remains costly and tim...

Predictions of protein-protein interactions and co-complex models with deep learning.

Protein-protein interactions (PPIs) are fundamental to cellular processes, and essential for understanding biological function and disease mechanisms....

Development and validation of a LightGBM-based model with risk stratification for predicting early recurrence after catheter ablation for atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is the most prevalent sustained arrhythmia, yet tools for predicting early recurrence (ER) after catheter ablatio...

Identification of diagnostic and prognostic hub genes in osteoarthritis: an integrated bioinformatics study.

BACKGROUND: Osteoarthritis (OA) is a heterogeneous whole-joint disease and a leading cause of pain and disability worldwide. Synovial inflammation pla...

Artificial intelligence in the clinical management and prognostication of mitral regurgitation: a systematic review.

BACKGROUND: Mitral regurgitation (MR), one of the most common valvular heart diseases, poses ongoing challenges in risk stratification and timely inte...

Deep learning-based automatic segmentation of rectal tumors in endoscopic images.

PURPOSE: Endoscopy is critical in the identification of rectal tumors, but is prone to observer errors. The aim of this study was to assess the inter-...

AI-assisted improvement of endoscopic diagnosis of ampullary lesions.

Ampullary neoplasms are often challenging to detect during forward-viewing esophagogastroduodenoscopy. This study aimed to evaluate the impact of arti...

SERPINA3 and NDRG1 are critical diagnostic immune genes associated with macrophages in preeclampsia.

Preeclampsia (PE) is a pregnancy complication involving immune dysregulation. This study aims to identify diagnostic immune biomarkers for PE using ma...

Generative artificial intelligence in endoscopy: distinguishing opportunity from risk.

Deciphering the potential molecular links between ochratoxin A and colorectal cancer: an integrated computational toxicological and single-cell transcriptomic study.

BACKGROUND: Ochratoxin A (OTA), a common foodborne mycotoxin, is classified as a potential human carcinogen. However, the specific molecular mechanism...

LightGastroFormer: a lightweight multi-resolution transformer for gastrointestinal disease classification.

In order to promote early diagnosis and lessen the workload of doctors during endoscopic and capsule endoscopy tests, automated analysis of gastrointe...

Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.

Artificial intelligence (AI) is rapidly transforming healthcare, supporting disease management and enabling outcome prediction across multiple clinica...

A histopathologically verified dataset of magnifying narrow-band imaging endoscopy for classifying the gastric precancerous cascade.

Gastric cancer is a leading cause of mortality worldwide, yet the development of computer-aided diagnosis (CAD) systems for its early detection is hin...

A novel deep learning network for small bowel ulcerative lesion detection and differential diagnosis on double-balloon endoscopy images.

Differentiating small bowel ulcerative diseases (SBUDs) on double-balloon endoscopy (DBE) is challenging. We aimed to develop an artificial intelligen...

Dual-stream token fusion with Swin Transformer and lesion-aware tokens for gastric metaplasia classification in IoMT-assisted deployment.

BACKGROUND: Gastric intestinal metaplasia (GIM) is often visually inconspicuous on routine endoscopy, while many artificial intelligence systems rely ...