Latest AI and machine learning research in inflammatory bowel disease for healthcare professionals.
Background Although CT-based body composition (BC) metrics may inform disease risk and outcomes, obtaining these metrics has been too resource intensive for large-scale use. Thus, population-wide distributions of BC remain uncertain. Purpose To demonstrate the validity of fully automated, deep learning BC analysis from abdominal CT examinations, to define demographically adjusted BC reference curv...
BACKGROUND: Hereditary colorectal cancer (HCRC) syndromes account for 10% of colorectal cancers but remain underdiagnosed. This feasibility project tested the utility of an artificial intelligence-based chatbot deployed to patients scheduled for colonoscopy to identify HCRC risk factors, educate participants about HCRC and obtain consent to genetic testing as an extension of genetic counselling of...
BACKGROUND: Parasellar dural invasion can be associated with treatment failure after excision of functioning pituitary adenomas. Because the medial wa...
PURPOSE: Accurate prediction of postoperative remission is beneficial for effective patient-physician communication in acromegalic patients. This stud...
Water exchange (WE) and artificial intelligence (AI) have made critical advances during the past decade. WE significantly increases adenoma detection ...
Some patients with acromegaly do not reach the remission standard in the short term after surgery but achieve remission without additional postoperat...
BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disease with periods of flares and remission. Designing personalized treatment strat...
BACKGROUND AND AIMS: Colonoscopy is commonly performed for colorectal cancer screening in the United States. Reports are often generated in a non-stan...
IMPORTANCE: Most patients with primary aldosteronism, a major cause of secondary hypertension, are not identified or appropriately treated because of ...
Artificial intelligence is currently a hot topic in medicine. However, medical data is often sparse and hard to obtain due to legal restrictions and l...
An essential aspect of medical research is the prediction for a health outcome and the scientific identification of important factors. As a result, nu...
METHODS: We collected and sorted out the white light endoscopic images of some patients undergoing colonoscopy. The convolutional neural network model...
BACKGROUND: As Crohn's disease (CD) is associated with a high risk of thromboembolic events (TE), including patients with subclinical inflammation, we...
Colorectal cancer has a great incidence rate worldwide, but its early detection significantly increases the survival rate. Colonoscopy is the gold sta...
Key variables recorded as text in colonoscopy and pathology reports have been extracted using natural language processing (NLP) tools that were not ea...
Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they...
A new method to predict coronary artery lesions (CALs) in Kawasaki disease (KD) was developed using a mean structure equation model (SEM) and neural n...
Analysis of colonoscopy images plays a significant role in early detection of colorectal cancer. Automated tissue segmentation can be useful for two o...
BACKGROUND: Severe bleeding and perforation of the colon and rectum are complications of ulcerative colitis which can be treated by a targeted drug de...