Peptic Ulcer Disease

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 critical limitation of these traditional existing approaches is their inability to concurrently discern and distinguish among diverse NSAIDs, presenting a notable gap in the analytical capabilities within this domain. Herein, a creative dual-channel fluores...

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 IFN therapy in patients with chronic hepatitis B (CHB). Our study aimed to investigate potential targets for IFN therapy and to explore the network of interactions associated with IFN response. MicroRNA (miRNA) (GSE29911) and messenger RNA (GSE27555) ...

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...

Use of Artificial Intelligence in Lower Gastrointestinal and Small Bowel Disorders: An Update Beyond Polyp Detection.

Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for dete...

Impact of standard enhancement settings of endoscopy systems on performance of endoscopic artificial intelligence systems.

BACKGROUND:  Artificial intelligence (AI) systems in endoscopy are predominantly developed and tested using high-quality imagery from expert centers. ...

Enhancing surgical precision in squamous cell carcinoma of the head and neck: Hyperspectral imaging and artificial intelligence for improved margin assessment in an ex vivo setting.

BACKGROUND: Head and neck cancers, constituting 3-5% of all cancer cases, often require surgical resection for optimal outcomes. Achieving complete re...

Ultrasound Predicts Drug-Induced Sleep Endoscopy Findings Using Machine Learning Models.

OBJECTIVES: Ultrasound is a promising low-risk imaging modality that can provide objective airway measurements that may circumvent limitations of drug...

Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning.

The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). Ho...