Inflammatory Bowel Disease

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 improve detection of colon polyps and adenomas by providing visual alarms during the procedure. We aimed to assess the effectiveness of a CADe system that avoids potential operational bias.

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 models that predict treatment outcomes for indivi...

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 turkeys to resist nonpathogenic or less pathogenic...

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 has been used as anticancer, Diabetes Mellitus and ...

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 in 2018, water exchange colonoscopy and artificial ...

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 effectiveness of a colonoscopy. Several tools, such a...

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 leading causes of cancer-related deaths on a global s...

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

OBJECTIVES: Simple, specific, accurate, precise, sensitive, and cost effective spectrophotometric methods were developed and validated for quantificat...

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 clinical features. However, objective laboratory bio...

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 polyp segmentation method has been presented.

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 sophisticated technology such as two-dimensional gas chromat...

The Use of Transdermal Estrogen in Castrate-resistant, Steroid-refractory Prostate Cancer.

BACKGROUND: Androgen-deprivation therapy is the mainstay of treatment for metastatic prostate cancer. Corticosteroids and estrogens are also useful ag...

Development of a real-time endoscopic image diagnosis support system using deep learning technology in colonoscopy.

Gaps in colonoscopy skills among endoscopists, primarily due to experience, have been identified, and solutions are critically needed. Hence, the deve...

Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach.

BACKGROUND: Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the out...

Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs.

BACKGROUND: Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related ...

Challenges Facing the Detection of Colonic Polyps: What Can Deep Learning Do?

Colorectal cancer (CRC) is one of the most common causes of cancer mortality in the world. The incidence is related to increases with age and western ...

Association of trough vedolizumab levels with clinical, biological and endoscopic outcomes during maintenance therapy in inflammatory bowel disease.

OBJECTIVE: To establish the relationship between trough vedolizumab levels and outcomes during maintenance therapy.

Non-steroid anti-inflammatory drugs reduce the efficacy of autologous blood pleurodesis.

BACKGROUND: This study aims to perform autologous blood pleurodesis in an animal model and investigate the effects of paracetamol and diclofenac on au...

Ontology-Based Interactive Visualization of Patient-Generated Research Questions.

BACKGROUND: Crohn's disease and colitis are chronic conditions that affect every facet of patients' lives (e.g., social interaction, family, work, die...