AIMC Topic: Capsule Endoscopy
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AI-assisted capsule endoscopy reading in suspected small bowel bleeding: a multicentre prospective study.
The Lancet. Digital health
May 1, 2024
BACKGROUND: Capsule endoscopy reading is time consuming, and readers are required to maintain attention so as not to miss significant findings. Deep convolutional neural networks can recognise relevant findings, possibly exceeding human performances ...
Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2024
BACKGROUND: Gastrointestinal tract (GIT) diseases impact the entire digestive system, spanning from the mouth to the anus. Wireless Capsule Endoscopy (WCE) stands out as an effective analytic instrument for Gastrointestinal tract diseases. Neverthele...
Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network.
Revista espanola de enfermedades digestivas
Feb 1, 2023
BACKGROUND AND AIMS: capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN),...
The scientific progress and prospects of artificial intelligence in digestive endoscopy: A comprehensive bibliometric analysis.
Medicine
Nov 25, 2022
Artificial intelligence (AI) has been used for diagnosis and outcome prediction in clinical practice. Furthermore, AI in digestive endoscopy has attracted much attention and shown promising and stimulating results. This study aimed to determine the d...
Enhanced segmentation of gastrointestinal polyps from capsule endoscopy images with artifacts using ensemble learning.
World journal of gastroenterology
Nov 7, 2022
BACKGROUND: Endoscopy artifacts are widespread in real capsule endoscopy (CE) images but not in high-quality standard datasets.
Identification of Ulcers and Erosions by the Novel Pillcam™ Crohn's Capsule Using a Convolutional Neural Network: A Multicentre Pilot Study.
Journal of Crohn's & colitis
Jan 28, 2022
BACKGROUND AND AIMS: Capsule endoscopy is a central element in the management of patients with suspected or known Crohn's disease. In 2017, PillCam™ Crohn's Capsule was introduced and demonstrated to have greater accuracy in the evaluation of extensi...
Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network.
BMJ open gastroenterology
Sep 1, 2021
OBJECTIVE: Capsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in t...
Automated Detection of Crohn's Disease Intestinal Strictures on Capsule Endoscopy Images Using Deep Neural Networks.
Journal of Crohn's & colitis
May 4, 2021
BACKGROUND AND AIMS: Passable intestinal strictures are frequently detected on capsule endoscopy [CE]. Such strictures are a major component of inflammatory scores. Deep neural network technology for CE is emerging. However, the ability of deep neura...
The exceptional performance of deep learning for capsule endoscopy: Will such quality be maintained in clinical scenarios?
Gastrointestinal endoscopy
Feb 1, 2021
Application of Artificial Intelligence in Gastrointestinal Endoscopy.
Journal of clinical gastroenterology
Feb 1, 2021
Artificial intelligence (AI), also known as computer-aided diagnosis, is a technology that enables machines to process information and functions at or above human level and has great potential in gastrointestinal endoscopy applications. At present, t...
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