Peptic Ulcer Disease

Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination.

The rapid deployment of COVID-19 vaccines has necessitated the ongoing surveillance of adverse events, with abnormal uterine bleeding (AUB) emerging as a reported concern in vaccinated females. We aimed to develop a machine learning (ML) model to predict post-vaccination AUB in women aged less than 50 years. A large-scale national cohort, the Korean Nationwide Cohort (K-COV-N cohort), was utilized...

Machine Learning-Based Mortality Prediction for Acute Gastrointestinal Bleeding Patients Admitted to Intensive Care Unit.

OBJECTIVE: The study aimed to develop machine learning (ML) models to predict the mortality of patients with acute gastrointestinal bleeding (AGIB) in the intensive care unit (ICU) and compared their prognostic performance with that of Acute Physiology and Chronic Health Evaluation II (APACHE-II) score.

Identification of potential biomarkers for lung cancer using integrated bioinformatics and machine learning approaches.

Lung cancer is one of the most common cancer and the leading cause of cancer-related death worldwide. Early detection of lung cancer can help reduce t...

An advanced robotic system incorporating haptic feedback for precision cardiac ablation procedures.

This study introduces an innovative master-slave cardiac ablation catheter robot system that employs magnetorheological fluids. The system incorporate...

Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.

Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alt...

Machine Learning-Assisted Chemical Tongues Based on Dual-channel Inclusion Complexes for Rapid Identification of Nonsteroidal Anti-inflammatory Drugs in Food.

The improper application of nonsteroidal anti-inflammatory drugs (NSAIDs) presents significant health hazards via vector food contamination. A critica...

Identification of Biomarkers for Response to Interferon in Chronic Hepatitis B Based on Bioinformatics Analysis and Machine Learning.

Interferon (IFN) is a pivotal agent against hepatitis B virus (HBV) in clinic, but there is a lack of accurate biomarkers to predict the response to I...

Machine learning using random forest to differentiate between blow and fall situations of head trauma.

Blunt head trauma is a common occurrence in forensic practice. Interpreting the origin of craniocerebral injuries can be a challenging process, partic...

New machine-learning models outperform conventional risk assessment tools in Gastrointestinal bleeding.

Rapid and accurate identification of high-risk acute gastrointestinal bleeding (GIB) patients is essential. We developed two machine-learning (ML) mod...

An Integrative Machine Learning Model for Predicting Early Safety Outcomes in Patients Undergoing Transcatheter Aortic Valve Implantation.

: Early safety outcomes following transcatheter aortic valve implantation (TAVI) for severe aortic stenosis are critical for patient prognosis. Accura...

Soft-tissue metastasis in esophageal cancer managed by dose escalation radiation therapy: a clinical case and review of literature.

Soft tissue metastasis in esophageal cancer is a very rare entity. A 76-year-old man was referred for a week's history of dysphagia. Upper gastrointes...

Democratizing cancer detection: artificial intelligence-enhanced endoscopy could address global disparities in head and neck cancer outcomes.

INTRODUCTION: This article explores the potential role of artificial intelligence (AI) in enhancing the early detection and diagnosis of head and neck...

Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.

Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap betw...

Unsupervised neural network-based image stitching method for bladder endoscopy.

Bladder endoscopy enables the observation of intravesical lesion characteristics, making it an essential tool in urology. Image stitching techniques a...

Combining machine learning with external validation to explore necroptosis and immune response in moyamoya disease.

Moyamoya disease (MMD) is a rare chronic vascular disease leads to cognitive impairment and stroke with its etiology unknown. The relationship between...

SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein function prediction.

BACKGROUND: A massive amount of protein sequences have been obtained, but their functions remain challenging to discern. In recent research on protein...

Evaluation of an artificial intelligence-based system for real-time high-quality photodocumentation during esophagogastroduodenoscopy.

Complete and high-quality photodocumentation in esophagoduodenogastroscopy (EGD) is essential for accurately diagnosing upper gastrointestinal disease...

Use of artificial intelligence in submucosal vessel detection during third-space endoscopy.

While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endosco...

Statistical and machine learning based platform-independent key genes identification for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most prevalent and deadly form of liver cancer, and its mortality rate is gradually increasing worldwide. Existi...

Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.

Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the n...