Inflammatory Bowel Disease

How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening.

Artificial intelligence may improve value in colonoscopy-based colorectal screening and surveillance...

Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.

Radiomics is an emerging field that involves extraction and quantification of features from medical ...

Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.

Radiomic features achieve promising results in cancer diagnosis, treatment response prediction, and ...

Comparison of Serum Concentrations of Ustekinumab Obtained by Three Commercial Assays in Patients with Crohn's Disease.

BACKGROUND: Data on the association of ustekinumab (UST) drug concentrations and clinical outcomes a...

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelop...

Interpretable Artificial Intelligence: Why and When.

The purpose of this article is to discuss the problem of interpretability of artificial intelligenc...

Big data in IBD: big progress for clinical practice.

IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic fa...

Deep learning uncertainty and confidence calibration for the five-class polyp classification from colonoscopy.

There are two challenges associated with the interpretability of deep learning models in medical ima...

Predicting individual clinical trajectories of depression with generative embedding.

Patients with major depressive disorder (MDD) show heterogeneous treatment response and highly varia...

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to...

Use of Artificial Intelligence-Based Analytics From Live Colonoscopies to Optimize the Quality of the Colonoscopy Examination in Real Time: Proof of Concept.

Predicting cognitive behavioral therapy outcome in the outpatient sector based on clinical routine data: A machine learning approach.

The availability of large-scale datasets and sophisticated machine learning tools enables developing...

Effect of experimental infectious bursal disease virus on clinical signs and pathogenesis of avian influenza virus HN in turkey by real time PCR.

Infectious bursal disease virus (IBDV) in turkeys may result in immunosuppression, and inability of ...

Effect of Phaleria Macrocarpa Flesh Fruits Extract on MDA Level, SGOT and SGPT Activity in Serum of Experimental Rats Contaminated by Cd (II) Ion.

BACKGROUND: Cd (II) ion is a heavy metal that has a toxic ability in the human body. P. macrocarpa h...

An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detection.

: Among the Gastrointestinal (GI) Endoscopy Editorial Board top 10 topics in advances in endoscopy i...

A novel artificial intelligence system for the assessment of bowel preparation (with video).

BACKGROUND AND AIMS: The quality of bowel preparation is an important factor that can affect the eff...

Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps.

Colorectal polyps are known to be potential precursors to colorectal cancer, which is one of the lea...

Spectrophotometric Quantification of Anti-inflammatory Drugs by Application of Chromogenic Reagents.

OBJECTIVES: Simple, specific, accurate, precise, sensitive, and cost effective spectrophotometric me...

The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches.

BACKGROUND: The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using cl...

Automated polyp segmentation for colonoscopy images: A method based on convolutional neural networks and ensemble learning.

PURPOSE: To automatically and efficiently segment the lesion area of the colonoscopy polyp image, a ...

Steroid identification via deep learning retention time predictions and two-dimensional gas chromatography-high resolution mass spectrometry.

Untargeted steroid identification represents a great analytical challenge even when using sophistica...