Gastroenterology

Inflammatory Bowel Disease

Latest AI and machine learning research in inflammatory bowel disease for healthcare professionals.

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HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.

Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse ...

A novel machine learning strategy for model selections - Stepwise Support Vector Machine (StepSVM).

An essential aspect of medical research is the prediction for a health outcome and the scientific id...

Application of Deep Learning for Early Screening of Colorectal Precancerous Lesions under White Light Endoscopy.

METHODS: We collected and sorted out the white light endoscopic images of some patients undergoing c...

Endoscopic activity, tissue factor and Crohn's disease: findings in clinical remission patients.

BACKGROUND: As Crohn's disease (CD) is associated with a high risk of thromboembolic events (TE), in...

Deep learning to find colorectal polyps in colonoscopy: A systematic literature review.

Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increa...

A Transparent and Adaptable Method to Extract Colonoscopy and Pathology Data Using Natural Language Processing.

Key variables recorded as text in colonoscopy and pathology reports have been extracted using natura...

An automated detection system for colonoscopy images using a dual encoder-decoder model.

Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture,...

Structure equation model and neural network analyses to predict coronary artery lesions in Kawasaki disease: a single-centre retrospective study.

A new method to predict coronary artery lesions (CALs) in Kawasaki disease (KD) was developed using ...

Polyp Segmentation with Fully Convolutional Deep Neural Networks-Extended Evaluation Study.

Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Aut...

Artificial Neural Network (ANN) Approach to Predict an Optimized pH-Dependent Mesalamine Matrix Tablet.

BACKGROUND: Severe bleeding and perforation of the colon and rectum are complications of ulcerative ...

Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study.

BACKGROUND AND AIMS: Up to 30% of adenomas might be missed during screening colonoscopy-these could ...

Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer.

Colonoscopy is commonly used to screen for colorectal cancer (CRC). We develop a deep learning model...

Regulatory considerations for artificial intelligence technologies in GI endoscopy.

Artificial intelligence (AI) technologies in clinical medicine have become the subject of intensive ...

Polyp detection algorithm can detect small polyps: Ex vivo reading test compared with endoscopists.

BACKGROUND AND STUDY AIMS: Small polyps are occasionally missed during colonoscopy. This study was c...

Predictors of remission from body dysmorphic disorder after internet-delivered cognitive behavior therapy: a machine learning approach.

BACKGROUND: Previous attempts to identify predictors of treatment outcomes in body dysmorphic disord...

What's new in IBD therapy: An "omics network" approach.

The industrial revolution that began in the late 1800s has resulted in dramatic changes in the envir...

Efficacy of Real-Time Computer-Aided Detection of Colorectal Neoplasia in a Randomized Trial.

BACKGROUND & AIMS: One-fourth of colorectal neoplasias are missed during screening colonoscopies; th...

Probing the degradation of pharmaceuticals in urine using MFC and studying their removal efficiency by UPLC-MS/MS.

Nutrient recovery from source-separated human urine has attracted interest as it is rich in nitrogen...

Deep learned tissue "fingerprints" classify breast cancers by ER/PR/Her2 status from H&E images.

Because histologic types are subjective and difficult to reproduce between pathologists, tissue morp...

The impact of deep convolutional neural network-based artificial intelligence on colonoscopy outcomes: A systematic review with meta-analysis.

BACKGROUND AND AIM: The utility of artificial intelligence (AI) in colonoscopy has gained popularity...

Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cere...

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