Peptic Ulcer Disease

Magnetic Torque-Driven All-Terrain Microrobots.

All-terrain microrobots possess significant potential in modern medical applications due to their superior maneuverability in complex terrains and confined spaces. However, conventional microrobots often struggle with adaptability and operational difficulties in variable environments. This study introduces a magnetic torque-driven all-terrain multiped microrobot (MTMR) to address these challenges....

ARViS: a bleed-free multi-site automated injection robot for accurate, fast, and dense delivery of virus to mouse and marmoset cerebral cortex.

Genetically encoded fluorescent sensors continue to be developed and improved. If they could be expressed across multiple cortical areas in non-human primates, it would be possible to measure a variety of spatiotemporal dynamics of primate-specific cortical activity. Here, we develop an Automated Robotic Virus injection System (ARViS) for broad expression of a biosensor. ARViS consists of two tech...

Building Machine Learning Models in Gastrointestinal Endoscopy.

The current landscape of machine learning models in GI endoscopy is fraught with considerable variability in methodologies and quality, posing challen...

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma ...

Automated Association for Osteosynthesis Foundation and Orthopedic Trauma Association classification of pelvic fractures on pelvic radiographs using deep learning.

High-energy impacts, like vehicle crashes or falls, can lead to pelvic ring injuries. Rapid diagnosis and treatment are crucial due to the risks of se...

Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?

INTRODUCTION: Artificial intelligence and large language models (LLMs) have emerged as potentially disruptive technologies in healthcare. In this stud...

Early prognosis prediction for non-variceal upper gastrointestinal bleeding in the intensive care unit: based on interpretable machine learning.

INTRODUCTION: This study aims to construct a mortality prediction model for patients with non-variceal upper gastrointestinal bleeding (NVUGIB) in the...

Role of artificial intelligence in Crohn's disease intestinal strictures and fibrosis.

Crohn's disease (CD) is a chronic inflammatory disorder of the gastrointestinal tract. Intestinal fibrosis or stricture is one of the most prevalent c...

Depth estimation from monocular endoscopy using simulation and image transfer approach.

Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space co...

Explainable machine learning for assessing upper respiratory tract of racehorses from endoscopy videos.

Laryngeal hemiplegia (LH) is a major upper respiratory tract (URT) complication in racehorses. Endoscopy imaging of horse throat is a gold standard fo...

Applying 12 machine learning algorithms and Non-negative Matrix Factorization for robust prediction of lupus nephritis.

Lupus nephritis (LN) is a challenging condition with limited diagnostic and treatment options. In this study, we applied 12 distinct machine learning ...

Polyp detection with colonoscopy assisted by the GI Genius artificial intelligence endoscopy module compared with standard colonoscopy in routine colonoscopy practice (COLO-DETECT): a multicentre, open-label, parallel-arm, pragmatic randomised controlled trial.

BACKGROUND: Increased polyp detection during colonoscopy is associated with decreased post-colonoscopy colorectal cancer incidence and mortality. The ...

AI-KODA score application for cleanliness assessment in video capsule endoscopy frames.

BACKGROUND: Currently, there is no automated method for assessing cleanliness in video capsule endoscopy (VCE). Our objectives were to automate the pr...

Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning.

Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment a...

Coagulation Risk Predicting in Anticoagulant-Free Continuous Renal Replacement Therapy.

INTRODUCTION: Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal blood purification therapy to replace impaired rena...

Lesion-Decoupling-Based Segmentation With Large-Scale Colon and Esophageal Datasets for Early Cancer Diagnosis.

Lesions of early cancers often show flat, small, and isochromatic characteristics in medical endoscopy images, which are difficult to be captured. By ...

Machine learning-assisted label-free colorectal cancer diagnosis using plasmonic needle-endoscopy system.

Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. Existing diagnostic techniques are often invasive ...

DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning.

BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (P...

Machine learning identifies key cells and therapeutic targets during ferroptosis after spinal cord injury.

JOURNAL/nrgr/04.03/01300535-202606000-00064/figure1/v/2026-02-11T151048Z/r/image-tiff Ferroptosis, a type of cell death that mainly involves iron meta...

Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms.

BACKGROUND: Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenter...