GERD

Deep Learning-Enabled Assessment of Right Ventricular Function Improves Prognostication After Transcatheter Edge-to-Edge Repair for Mitral Regurgitation.

BACKGROUND: Right ventricular (RV) function has a well-established prognostic role in patients with severe mitral regurgitation (MR) undergoing transcatheter edge-to-edge repair (TEER) and is typically assessed using echocardiography-measured tricuspid annular plane systolic excursion. Recently, a deep learning model has been proposed that accurately predicts RV ejection fraction (RVEF) from 2-dim...

Machine learning classification and biochemical characteristics in the real-time diagnosis of gastric adenocarcinoma using Raman spectroscopy.

This study aimed to identify biomolecular differences between benign gastric tissues (gastritis/intestinal metaplasia) and gastric adenocarcinoma and to evaluate the diagnostic power of Raman spectroscopy-based machine learning in gastric adenocarcinoma. Raman spectroscopy-based machine learning was applied in real-time during endoscopy in 19 patients (aged 51-85 years) with high-risk for gastric ...

PPILS: Protein-protein interaction prediction with language of biological coding.

Protein-protein interactions within a cell are essential for various fundamental biological processes. Computational techniques have arisen in bioinfo...

Natural Language Processing in Gastroenterology: Current Applications and Future Directions.

Natural language processing (NLP), a branch of artificial intelligence, has rapidly gained importance in health care for converting unstructured clini...

The Role of Artificial Intelligence for Advanced Endoscopy.

Artificial intelligence (AI) application in gastroenterology has grown in the last decade and continues to evolve very rapidly. Early promising result...

Artificial Intelligence in Endoscopy Quality Measures.

Quality of gastrointestinal endoscopy is a major determinant of its effectiveness. Artificial intelligence (AI) has the potential to enhance quality m...

Impact of Artificial Intelligence on Gastroenterology Trainee Education.

Artificial intelligence (AI) is transforming gastroenterology, particularly in endoscopy, which has a direct impact on trainees and their education. A...

Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.

Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying h...

The Role of Industry to Grow Clinical Artificial Intelligence Applications in Gastroenterology and Endoscopy.

The integration of artificial intelligence (AI) in health care has the potential to enhance diagnostics and disease management. In the field of gastro...

Reducing reading time and assessing disease in capsule endoscopy videos: A deep learning approach.

BACKGROUND: The wireless capsule endoscope (CE) is a valuable diagnostic tool in gastroenterology, offering a safe and minimally invasive visualizatio...

Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.

Fluorescence imaging has been widely used in fields like (pre)clinical imaging and other domains. With advancements in imaging technology and new fluo...

Predictive value of CT-based and AI-reconstructed 3D-TAPSE in patients undergoing transcatheter tricuspid valve repair.

BACKGROUND: The tricuspid annular plane systolic excursion (TAPSE) assessed by echocardiography has failed in predicting outcomes in patients with sev...

Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.

BACKGROUND: We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on e...

The Use of Artificial Intelligence for Endoscopic Evaluation of the Small Bowel.

There remains great potential for widespread implementation of artificial intelligence (AI) in managing small bowel disorders. Studies have shown exce...

A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study...

MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.

Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these...

Meta-Learning Enables Complex Cluster-Specific Few-Shot Binding Affinity Prediction for Protein-Protein Interactions.

Predicting protein-protein interaction (PPI) binding affinities in unseen protein complex clusters is essential for elucidating complex protein intera...

Deciphering Necroptosis-Associated Molecular Subtypes in Acute Ischemic Stroke Through Bioinformatics and Machine Learning Analysis.

Acute ischemic stroke (AIS) is a severe disorder characterized by complex pathophysiological processes, which can lead to disability and death. This s...

Tumor detection on bronchoscopic images by unsupervised learning.

The diagnosis and early identification of intratracheal tumors relies on the experience of the operators and the specialists. Operations by physicians...

Deep learning-enabled filter-free fluorescence microscope.

Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and s...