Inflammatory Bowel Disease

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 outcome for individual patients in the first clinical contact. In this study, we aimed to build multivariable prediction models of 1-year remission and recovery outcomes using baseline clinical variables in people with first-episode psychosis.

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 differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a person's age and self-reported sex using only 12-lead ECG signals. We further hypothesized that dis...

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...

Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study.

BACKGROUND AND PURPOSE: 3D reconstruction of a targeted area ("safe" triangle and Kambin triangle) may benefit the viability assessment of transforami...

Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

IMPORTANCE: Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy...

Prediction of Hemolytic Toxicity for Saponins by Machine-Learning Methods.

Saponins are a type of compounds bearing a hydrophobic steroid/triterpenoid moiety and hydrophilic carbohydrate branches. The majority of the saponins...

Improving Automatic Polyp Detection Using CNN by Exploiting Temporal Dependency in Colonoscopy Video.

Automatic polyp detection has been shown to be difficult due to various polyp-like structures in the colon and high interclass variations in polyp siz...

Quality assurance of computer-aided detection and diagnosis in colonoscopy.

Recent breakthroughs in artificial intelligence (AI), specifically via its emerging sub-field "deep learning," have direct implications for computer-a...

Predicting one-year outcome in first episode psychosis using machine learning.

BACKGROUND: Early illness course correlates with long-term outcome in psychosis. Accurate prediction could allow more focused intervention. Earlier in...

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for r...

Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning.

BACKGROUND: Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populatio...

Artificial intelligence and colonoscopy: Current status and future perspectives.

BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endos...

Potential roles of artificial intelligence learning and faecal immunochemical testing for prioritisation of colonoscopy in anaemia.

Iron deficiency anaemia (IDA) is the most common cause of anaemia and a frequent indication for colonoscopy, although the prevalence of colorectal can...

Effects of Progesterone and 17β-Estradiol under Presence or Absence of FBS on Plasminogen Activators Activity in Porcine Uterine Epithelial Cells.

The present study was conducted to investigate the regulatory mechanism of plasminogen activators (PAs) activation by 17β-estradiol (E) and progestero...

Association of time to colonoscopy after a positive fecal test result and fecal hemoglobin concentration with risk of advanced colorectal neoplasia.

BACKGROUND: We evaluated the risk of advanced colorectal neoplasia (ACRN) and colorectal cancer (CRC) according to time to colonoscopy after positive ...

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%, ...

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 infor...