Inflammatory Bowel Disease

Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions.

Computer-aided diagnosis offers a promising solution to reduce variation in colonoscopy performance. Pooled miss rates for polyps are as high as 22%, and associated interval colorectal cancers after colonoscopy are of concern. Optical biopsy, whereby in-vivo classification of polyps based on enhanced imaging replaces histopathology, has not been incorporated into routine practice because it is lim...

Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag).

Adenomatous polyps are a common precursor lesion for colorectal cancer. ColonFlag is a machine- learning-based algorithm that uses basic patient information and complete blood cell counts (CBC) to identify individuals at elevated risk of colorectal cancer for intensified screening. The purpose of this study was to determine whether ColonFlag is also able to predict the presence of high risk adenom...

Level of circulating steroid hormones in malaria and cutaneous leishmaniasis: a case control study.

Epidemiological and clinical studies have shown a great difference in the severity and prevalence of infectious diseases in men and women and various ...

Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach.

Many variables have been linked to different course trajectories of depression. These findings, however, are based on group comparisons with unknown t...

Human-machine collaboration: bringing artificial intelligence into colonoscopy.

Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy.

The detection and removal of precancerous polyps via colonoscopy is the gold standard for the prevention of colon cancer. However, the detection rate ...

Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy: A Prospective Study.

BACKGROUND: Computer-aided diagnosis (CAD) for colonoscopy may help endoscopists distinguish neoplastic polyps (adenomas) requiring resection from non...

Using Machine Learning to Aid the Interpretation of Urine Steroid Profiles.

BACKGROUND: Urine steroid profiles are used in clinical practice for the diagnosis and monitoring of disorders of steroidogenesis and adrenal patholog...

Estimating risk of severe neonatal morbidity in preterm births under 32 weeks of gestation.

A large recent study analyzed the relationship between multiple factors and neonatal outcome and in preterm births. Study variables included the reas...

Preparation of Molecularly Imprinted Microspheres as Biomimetic Recognition Material for In Situ Adsorption and Selective Chemiluminescence Determination of Bisphenol A.

Bisphenol A (BPA) is an endocrine disrupter in environments which can induce abnormal differentiation of reproductive organs by interfering with the a...

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.

BACKGROUND & AIMS: The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect the...

Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy.

Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal can...

Predictive modeling of treatment resistant depression using data from STAR*D and an independent clinical study.

Identification of risk factors of treatment resistance may be useful to guide treatment selection, avoid inefficient trial-and-error, and improve majo...

Plasminogen activator inhibitor-1 is associated with the metabolism and development of advanced colonic polyps.

Implications of plasminogen activator inhibitor-1 (PAI-1) in colonic polyps remain elusive. A prospective study was conducted with 188 consecutive sub...

Modeling asynchronous event sequences with RNNs.

Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly addr...

Unsupervised Reverse Domain Adaptation for Synthetic Medical Images via Adversarial Training.

To realize the full potential of deep learning for medical imaging, large annotated datasets are required for training. Such datasets are difficult to...

Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology.

INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biol...

Vitamin D Deficiency in a Portuguese Cohort of Patients with Inflammatory Bowel Disease: Prevalence and Relation to Disease Activity.

BACKGROUND AND AIMS: Vitamin D deficiency is more common in inflammatory bowel disease (IBD) patients than in the general population. However, there a...

Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.

Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are u...

Natural Language Processing Accurately Calculates Adenoma and Sessile Serrated Polyp Detection Rates.

BACKGROUND: ADR is a widely used colonoscopy quality indicator. Calculation of ADR is labor-intensive and cumbersome using current electronic medical ...