AIMC Topic: Colonoscopy

Clear Filters Showing 131 to 140 of 353 articles

Deep learning approach to detection of colonoscopic information from unstructured reports.

BMC medical informatics and decision making
BACKGROUND: Colorectal cancer is a leading cause of cancer deaths. Several screening tests, such as colonoscopy, can be used to find polyps or colorectal cancer. Colonoscopy reports are often written in unstructured narrative text. The information em...

Intraprocedure Artificial Intelligence Alert System for Colonoscopy Examination.

Sensors (Basel, Switzerland)
Colonoscopy is a valuable tool for preventing and reducing the incidence and mortality of colorectal cancer. Although several computer-aided colorectal polyp detection and diagnosis systems have been proposed for clinical application, many remain sus...

Polyp characterization using deep learning and a publicly accessible polyp video database.

Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
OBJECTIVES: Convolutional neural networks (CNN) for computer-aided diagnosis of polyps are often trained using high-quality still images in a single chromoendoscopy imaging modality with sessile serrated lesions (SSLs) often excluded. This study deve...

Improving colorectal cancer screening - consumer-centred technological interventions to enhance engagement and participation amongst diverse cohorts.

Clinics and research in hepatology and gastroenterology
The current "Gold Standard" colorectal cancer (CRC) screening approach of faecal occult blood test (FOBT) with follow-up colonoscopy has been shown to significantly improve morbidity and mortality, by enabling the early detection of disease. However,...

Computer-aided classification of colorectal segments during colonoscopy: a deep learning approach based on images of a magnetic endoscopic positioning device.

Scandinavian journal of gastroenterology
OBJECTIVE: Assessment of the anatomical colorectal segment of polyps during colonoscopy is important for treatment and follow-up strategies, but is largely operator dependent. This feasibility study aimed to assess whether, using images of a magnetic...

Deep learning-based automated quantification of goblet cell mucus using histological images as a predictor of clinical relapse of ulcerative colitis with endoscopic remission.

Journal of gastroenterology
BACKGROUND: Mucin depletion is one of the histological indicators of clinical relapse among patients with ulcerative colitis (UC). Mucin depletion is evaluated semiquantitatively by pathologists using histological images. Therefore, the interobserver...

Comprehensive review of publicly available colonoscopic imaging databases for artificial intelligence research: availability, accessibility, and usability.

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Publicly available databases containing colonoscopic imaging data are valuable resources for artificial intelligence (AI) research. Currently, little is known regarding the available number and content of these databases. This re...

Acute colonic flexures: the basis for developing an artificial intelligence-based tool for predicting the course of colonoscopy.

Anatomical science international
Tortuosity of the colon is an important parameter for predicting the course of colonoscopy. Computed tomography scans of the abdominal cavity were performed in 224 (94 female, 130 male) adult subjects. The number of acute (angle not exceeding 90°) be...

UC-NfNet: Deep learning-enabled assessment of ulcerative colitis from colonoscopy images.

Medical image analysis
Ulcerative colitis (UC) belongs to the inflammatory bowel disease (IBD) family, which is mainly caused by inflammation of the tissue in the colon and rectum. The severity of this infection can radically affect the patient's overall well-being. Althou...