Peptic Ulcer Disease

Preoperative Factors Associated With In-Hospital Major Bleeding After Percutaneous Coronary Intervention: A Systematic Review.

BACKGROUND: Preoperative risk assessment of bleeding after percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and, most importantly, for clinical decision-making. This systematic review aims to summarise preoperative factors associated with post-PCI in-hospital major bleeding.

Application of an Automated Deep Learning Program to A Diagnostic Classification Model: Differentiating High-Risk Adenomas Among Colorectal Polyps 10 mm or Smaller.

OBJECTIVE: This study aimed to develop a computer-aided diagnosis (CADx) model using an automated deep learning (DL) program to classify low- and high-risk adenomas among colorectal polyps ≤ 10 mm with standard white-light endoscopy.

Identification of biomarkers associated with M1 macrophages in the ST-segment elevation myocardial infarction through bioinformatics and machine learning approaches.

ST-segment elevation myocardial infarction (STEMI) is considered a critical cardiac condition with a poor prognosis. Shortly after STEMI occurs, the i...

A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images.

Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagn...

Predicting protein-protein interaction with interpretable bilinear attention network.

BACKGROUND AND OBJECTIVE: Protein-protein interactions (PPIs) play the key roles in myriad biological processes, helping to understand the protein fun...

Natural language processing for identifying major bleeding risk in hospitalised medical patients.

BACKGROUND: Major bleeding is a severe complication in critically ill medical patients, resulting in significant morbidity, mortality, and healthcare ...

Exploring new drug treatment targets for immune related bone diseases using a multi omics joint analysis strategy.

In the field of treatment and prevention of immune-related bone diseases, significant challenges persist, necessitating the urgent exploration of new ...

Development and Validation of Models for Preoperative Prediction of Risk and Postoperative Detection of Noninfectious Complications Using Interpretable Machine Learning and Electronic Health Record Data.

OBJECTIVE: To apply interpretable machine learning methodology to electronic health record data to develop models for preoperative risk estimation and...

Development and validation of a machine learning model to predict hemostatic intervention in patients with acute upper gastrointestinal bleeding.

BACKGROUND: Acute upper gastrointestinal bleeding (UGIB) is common in clinical practice and has a wide range of severity. Along with medical therapy, ...

Development of an artificial intelligence-enhanced warfarin interaction checker platform.

Warfarin is a common anticoagulant drug for thrombo-prophylaxis in stroke and venous thromboembolism, which has many advantages but also some disadvan...

Precision Risk Stratification in Atrial Fibrillation: Evaluating Machine Learning Models for Bleeding Prediction and Clinical Integration.

Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia.

Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, cal...

Artificial Intelligence-Assisted Capsule Endoscopy Versus Conventional Capsule Endoscopy for Detection of Small Bowel Lesions: A Systematic Review and Meta-Analysis.

BACKGROUND: Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the l...

Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.

Classifying medical images is essential in computer-aided diagnosis (CAD). Although the recent success of deep learning in the classification tasks ha...

NaMA-Mamba: Foundation model for generalizable nasal disease detection using masked autoencoder with Mamba on endoscopic images.

Artificial intelligence (AI) has shown great promise in analyzing nasal endoscopic images for disease detection. However, current AI systems require e...

Liquid-bodied antibiofilm robot with switchable viscoelastic response for biofilm eradication on complex surface topographies.

Recalcitrant biofilm infections pose a great challenge to human health. Micro- and nanorobots have been used to eliminate biofilm infections in hard-t...

Using Machine Learning Models to Diagnose Chronic Rhinosinusitis: Analysis of Pre-Treatment Patient-Generated Health Data to Predict Cardinal Symptoms and Sinonasal Inflammation.

BackgroundThe diagnosis of chronic rhinosinusitis (CRS) relies upon patient-reported symptoms and objective nasal endoscopy and/or computed tomography...

Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study.

BACKGROUND: Gastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting and lacks tailored risk assessment tools for perso...

Diagnostic accuracy of convolutional neural network algorithms to distinguish gastrointestinal obstruction on conventional radiographs in a pediatric population.

PURPOSE: Gastrointestinal (GI) dilatations are frequently observed in radiographs of pediatric patients who visit emergency departments with acute sym...

Improving the Real-Time Classification of Disease Severity in Ulcerative Colitis: Artificial Intelligence as the Trigger for a Second Opinion.

INTRODUCTION: Endoscopic classification of ulcerative colitis (UC) shows high interobserver variation. Previous research demonstrated that artificial ...