Gastroenterology

Peptic Ulcer Disease

Latest AI and machine learning research in peptic ulcer disease for healthcare professionals.

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Showing 43-63 of 3,581 articles
Ultrahigh-Throughput Virtual Screening Strategies against PPI Targets: A Case Study of STAT Inhibitors.

In recent years, virtual screening of ultralarge (10) libraries of synthetically accessible compound...

eNCApsulate: neural cellular automata for precision diagnosis on capsule endoscopes.

PURPOSE: Wireless capsule endoscopy (WCE) is a noninvasive imaging method for the entire gastrointes...

Identification and single-cell analysis of prognostic genes related to mitochondrial and neutrophil extracellular traps in bladder cancer.

The development of bladder cancer (BLCA) is associated with mitochondrial dysfunction and neutrophil...

Identifying features of prior hemorrhage in cerebral cavernous malformations on quantitative susceptibility maps: a machine learning pilot study.

Features of new bleeding on conventional imaging in cerebral cavernous malformations (CCMs) often di...

Evolving Role of Artificial Intelligence in Endoscopic Management of Inflammatory Bowel Disease: Diagnosis, Surveillance, and Assessment.

Inflammatory bowel disease (IBD), including Crohn's disease and ulcerative colitis, presents substan...

Predicting semantic segmentation quality in laryngeal endoscopy images.

Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial inte...

Integrating machine learning and bioinformatics approaches to identify novel diagnostic gene biomarkers for diabetic mice.

Diabetes is a complex metabolic disorder, and its pathogenesis involves the interplay of genetic, en...

A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions.

In recent years, artificial intelligence (AI) has been widely explored to enhance capsule endoscopy ...

Identification of CXCR4 as a potential preventive gene in clear cell renal cell carcinoma from machine learning and immune analysis.

Clear cell renal cell carcinoma (ccRCC) represents a prevalent malignant kidney tumor characterized ...

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in he...

Human protein interaction networks of ancestral and variant SARS-CoV-2 in organ-specific cells and bodily fluids.

Understanding SARS-CoV-2 human protein-protein interactions (PPIs) and the host response to infectio...

Artificial Intelligence in Gastrointestinal Endoscopy: The Japan Gastroenterological Endoscopy Society Position Statements.

Research and development of artificial intelligence (AI) in the field of gastrointestinal endoscopy ...

Machine-Learning Prediction of Bleeding After Endoscopic Submucosal Dissection for Early Gastric Cancer: A Multicenter Study.

BACKGROUND: Endoscopic submucosal dissection (ESD) is a minimally invasive treatment for early gastr...

Deep learning for classification of aggressive versus non-aggressive central giant cell granuloma using whole-slide histopathology images.

Microscopic images of aggressive and non-aggressive cases of central giant cell granuloma (CGCG) wer...

Diagnosis, clinical management, and emerging strategies for coronary artery disease in patients with cancer.

The intersection of cancer and coronary artery disease (CAD) presents complex clinical challenges re...

Photocatalytic microrobots for treating bacterial infections deep within sinuses.

Microrobotic techniques are promising for treating biofilm infections located deep within the human ...

Safety of nonselective nonsteroidal anti-inflammatory drugs in cardiac surgery: a historical cohort study.

PURPOSE: Pain management after cardiac surgery is imperative, as inadequate analgesia can increase t...

Towards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention.

While static risk models may identify key driving risk factors, the dynamic nature of risk requires ...

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