GERD

Human and Deep Learning Predictions of Peripheral Lung Cancer Using a 1.3 mm Video Endoscopic Probe.

BACKGROUND AND OBJECTIVE: Iriscope, a 1.3 mm video endoscopic probe introduced through an r-EBUS catheter, allows for the direct visualisation of small peripheral pulmonary nodules (PPNs). This study assessed the ability of physicians with different levels of experience in bronchoscopy, and the ability of artificial intelligence (AI) to predict the malignant nature of small PPNs during Iriscope pe...

AI-assisted multi-OMICS analysis reveals new markers for the prediction of AD.

Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder, characterized by progressive cognitive decline. Early and accurate diagnosis is crucial for improving patient outcomes, yet current diagnostic methods remain invasive, costly, and limited in accessibility. This study leverages artificial intelligence (AI) and machine learning approaches to perform a multi-omics analysis, in...

Predicting the Compressive Properties of Carbon Foam Using Artificial Neural Networks.

This article focusses on predicting the compressive properties of polyurethane-derived carbon foam using an artificial neural network (ANN) approach. ...

Machine learning to predict de novo protein-protein interactions.

Advances in machine learning for structural biology have dramatically enhanced our capacity to predict protein-protein interactions (PPIs). Here, we r...

Deep ensemble framework with Bayesian optimization for multi-lesion recognition in capsule endoscopy images.

In order to address the challenges posed by the large number of images acquired during wireless capsule endoscopy examinations and fatigue-induced lea...

Integrative machine learning and bioinformatics analysis unveil key genes for precise glioma classification and prognosis evaluation.

Gliomas exhibit significant heterogeneity and diverse molecular subtypes, and there are marked differences in treatment strategies and prognoses for g...

Recent advancement in size measurement during endoscopy.

Accurate lesion size measurement is essential in endoscopic practice as it influences treatment strategies, surveillance decisions, and clinical outco...

Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches.

Dysregulation of hepatic metabolism is a crucial factor in the development of fatty liver disease and significantly increases the risk of hepatocellul...

Using ML techniques to predict extubation outcomes for patients with central nervous system injuries in the Yun-Gui Plateau.

No predictive models have been reported for tracheostomy extubation success in plateau region rehabilitation departments. Hence, the primary objective...

Diagnostic Methods and Biomarkers in Inflammatory Bowel Disease.

Inflammatory bowel disease (IBD) refers to a chronic inflammatory condition involving the GI tract that includes Crohn's disease (CD) and ulcerative c...

Enhancing gastroenterology with multimodal learning: the role of large language model chatbots in digestive endoscopy.

INTRODUCTION: Advancements in artificial intelligence (AI) and large language models (LLMs) have the potential to revolutionize digestive endoscopy by...

Galar - a large multi-label video capsule endoscopy dataset.

Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitation...

Future of image enhanced endoscopy of esophageal adenocarcinoma.

Barrett's esophagus is a premalignant precursor lesion of esophageal adenocarcinoma that affects approximately 1% of the population worldwide. Esophag...

Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models.

Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), significantly affecting patient prognosis and increasing h...

Transforming Gastrointestinal Diagnosis with Molecular Endoscopy: Challenges and Opportunities.

Molecular endoscopy represents a transformative advance in the detection, diagnosis, and management of gastrointestinal diseases, addressing the criti...

Seeing through the leak: a global perspective on aortic regurgitation assessment.

AIMS: Despite established guidelines, the echocardiographic quantification of aortic regurgitation (AR) remains challenging in clinical practice. Whil...

Cardiovascular imaging in 2024: review of current research and innovations.

Cardiovascular imaging saw significant advancements in 2024, impacting technology, pathophysiology, and clinical applications. This review provides a ...

Technology advances in the placement of naso-enteral tubes and in the management of enteral feeding in critically ill patients: A narrative study.

Enteral feeding needs secure access to the upper gastrointestinal tract, an evaluation of the gastric function to detect gastrointestinal intolerance,...

Exploring potential diagnostic markers and therapeutic targets for type 2 diabetes mellitus with major depressive disorder through bioinformatics and in vivo experiments.

Type 2 diabetes mellitus (T2DM) and Major depressive disorder (MDD) act as risk factors for each other, and the comorbidity of both significantly incr...

Utility of convolutional neural network-enhanced electrocardiogram to diagnose and predict mitral regurgitation in patients with chronic atrial fibrillation.

A convolutional neural network (CNN)-enhanced electrocardiogram (ECG) has been reported for detecting mitral regurgitation (MR). This tool may be part...