AIMC Topic: Ulcer
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Deep learning-based lesion detection and severity grading of small-bowel Crohn's disease ulcers on double-balloon endoscopy images.
Gastrointestinal endoscopy
Dec 6, 2023
BACKGROUND AND AIMS: Double-balloon endoscopy (DBE) is widely used in diagnosing small-bowel Crohn's disease (CD). However, CD misdiagnosis frequently occurs if inexperienced endoscopists cannot accurately detect the lesions. The CD evaluation may al...
Deep Learning and Minimally Invasive Endoscopy: Automatic Classification of Pleomorphic Gastric Lesions in Capsule Endoscopy.
Clinical and translational gastroenterology
Oct 1, 2023
INTRODUCTION: Capsule endoscopy (CE) is a minimally invasive examination for evaluating the gastrointestinal tract. However, its diagnostic yield for detecting gastric lesions is suboptimal. Convolutional neural networks (CNNs) are artificial intelli...
Deep learning in negative small-bowel capsule endoscopy improves small-bowel lesion detection and diagnostic yield.
Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society
Sep 13, 2023
OBJECTIVES: Although several studies have shown the usefulness of artificial intelligence to identify abnormalities in small-bowel capsule endoscopy (SBCE) images, few studies have proven its actual clinical usefulness. Thus, the aim of this study wa...
Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions.
Medicina (Kaunas, Lithuania)
Jan 15, 2023
: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn's disease. Although the application...
Effectiveness of Semi-Supervised Active Learning in Automated Wound Image Segmentation.
International journal of molecular sciences
Dec 31, 2022
Appropriate wound management shortens the healing times and reduces the management costs, benefiting the patient in physical terms and potentially reducing the healthcare system's economic burden. Among the instrumental measurement methods, the image...
Automated detection of ulcers and erosions in capsule endoscopy images using a convolutional neural network.
Medical & biological engineering & computing
Jan 17, 2022
Capsule endoscopy (CE) is an important tool in the management of patients with known or suspected inflammatory bowel disease. Ulcers and erosions of the enteric mucosa are prevalent findings in these patients. They frequently occur together, and thei...
Emergent robotic versus laparoscopic surgery for perforated gastrojejunal ulcers: a retrospective cohort study of 44 patients.
Surgical endoscopy
Mar 24, 2021
BACKGROUND: Perforated gastrojejunal ulcers are a known complication following Roux-en-Y gastric bypass (RYGB) surgery requiring emergent surgical repair. The robotic approach has not been evaluated for emergency general surgery.
Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.
The Annals of otology, rhinology, and laryngology
Aug 14, 2020
OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annot...
A systematic evaluation and optimization of automatic detection of ulcers in wireless capsule endoscopy on a large dataset using deep convolutional neural networks.
Physics in medicine and biology
Dec 5, 2019
Compared with conventional gastroscopy which is invasive and painful, wireless capsule endoscopy (WCE) can provide noninvasive examination of gastrointestinal (GI) tract. The WCE video can effectively support physicians to reach a diagnostic decision...
Deep learning algorithms for automated detection of Crohn's disease ulcers by video capsule endoscopy.
Gastrointestinal endoscopy
Nov 16, 2019
BACKGROUND AND AIMS: The aim of our study was to develop and evaluate a deep learning algorithm for the automated detection of small-bowel ulcers in Crohn's disease (CD) on capsule endoscopy (CE) images of individual patients.
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