GERD

Assisted documentation as a new focus for artificial intelligence in endoscopy: the precedent of reliable withdrawal time and image reporting.

BACKGROUND : Reliable documentation is essential for maintaining quality standards in endoscopy; however, in clinical practice, report quality varies. We developed an artificial intelligence (AI)-based prototype for the measurement of withdrawal and intervention times, and automatic photodocumentation. METHOD:  A multiclass deep learning algorithm distinguishing different endoscopic image content ...

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.

Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate bi...

Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavil...

Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer.

Targeted therapies in cancer treatment can improve efficacy and reduce adverse effects by altering the tissue exposure of specific biomolecules. Howe...

Mastectomy with Reconstruction Including Robotic Endoscopic Surgery (MARRES): a prospective cohort study of the Korea Robot-Endoscopy Minimal Access Breast Surgery Study Group (KoREa-BSG) and Korean Breast Cancer Study Group (KBCSG).

BACKGROUND: Robotic nipple-sparing mastectomy (RNSM) has emerged as a new treatment option for breast cancer and risk-reducing mastectomy (RRM) for wo...

Artificial intelligence-aided optical imaging for cancer theranostics.

The use of artificial intelligence (AI) to assist biomedical imaging have demonstrated its high accuracy and high efficiency in medical decision-makin...

Geometric graph neural networks on multi-omics data to predict cancer survival outcomes.

The advance of sequencing technologies has enabled a thorough molecular characterization of the genome in human cancers. To improve patient prognosis ...

Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions.

Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways....

Protein-Protein Interaction Sites Prediction Using Batch Normalization Based CNNs and Oversampling Method Borderline-SMOTE.

The recognition of protein-protein interaction sites (PPIs) is beneficial for the interpretation of protein functions and the development of new drugs...

Applying deep learning to iterative screening of medium-sized molecules for protein-protein interaction-targeted drug discovery.

We combined a library of medium-sized molecules with iterative screening using multiple machine learning algorithms that were ligand-based, which resu...

Using a stacked ensemble learning framework to predict modulators of protein-protein interactions.

Identifying small molecule protein-protein interaction modulators (PPIMs) is a highly promising and meaningful research direction for drug discovery, ...

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review.

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. At early stages, CVDs appear with minor s...

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches.

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the...

Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy.

: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel clean...

Deep learning on graphs for multi-omics classification of COPD.

Network approaches have successfully been used to help reveal complex mechanisms of diseases including Chronic Obstructive Pulmonary Disease (COPD). H...

Machine Learning Predicts the Oxidative Stress Subtypes Provide an Innovative Insight into Colorectal Cancer.

So far, it has been reached the academic consensus that the molecular subtypes are via genomic heterogeneity and immune infiltration patterns. Conside...

The ethical matrix as a method for involving people living with disease and the wider public (PPI) in near-term artificial intelligence research.

INTRODUCTION: The rapid pace of research in the field of Artificial Intelligence in medicine has associated risks for near-term AI. Ethical considerat...

Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State-of-the-Art Review.

OBJECTIVE: The endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesio...

The Pros and Cons of Artificial Intelligence in Endoscopy.

A New Frontier: No Working Port for Robotic Mitral Valve Repair.

A 61-year-old male presented via referral for mitral regurgitation and was deemed an appropriate robotic surgery candidate for complex mitral valve re...