AIMC Topic: Capsule Endoscopy
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Toward automatic and reliable evaluation of human gastric motility using magnetically controlled capsule endoscope and deep learning.
Scientific reports
Jul 17, 2025
In this paper, we develop a combination of algorithms, including camera motion detector (CMD), deep learning models, class activation mapping (CAM), and periodical feature detector for the purpose of evaluating human gastric motility by detecting the...
A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions.
Scientific reports
Jul 2, 2025
In recent years, artificial intelligence (AI) has been widely explored to enhance capsule endoscopy (CE), with the goal of improving the efficiency of the reading process. While most AI models have been developed for small bowel and colon analysis, t...
Biomarker risk stratification with capsule sponge in the surveillance of Barrett's oesophagus: prospective evaluation of UK real-world implementation.
Lancet (London, England)
Jun 23, 2025
BACKGROUND: Endoscopic surveillance is the clinical standard for Barrett's oesophagus, but its effectiveness is inconsistent. We have developed a test comprising a pan-oesophageal cell collection device coupled with biomarkers to stratify patients in...
Advancing artificial intelligence applicability in endoscopy through source-agnostic camera signal extraction from endoscopic images.
PloS one
Jun 11, 2025
INTRODUCTION: Successful application of artificial intelligence (AI) in endoscopy requires effective image processing. Yet, the plethora of sources for endoscopic images, such as different processor-endoscope combinations or capsule endoscopy devices...
Galar - a large multi-label video capsule endoscopy dataset.
Scientific data
May 20, 2025
Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitations as well (time-consuming analysis, short battery lifetime, and poor image quality). Artificial inte...
A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images.
BMC medical informatics and decision making
Mar 31, 2025
Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imbalance, and computat...
Artificial Intelligence-Assisted Capsule Endoscopy Versus Conventional Capsule Endoscopy for Detection of Small Bowel Lesions: A Systematic Review and Meta-Analysis.
Journal of gastroenterology and hepatology
Mar 13, 2025
BACKGROUND: Capsule endoscopy (CE) is a valuable tool used in the diagnosis of small intestinal lesions. The study aims to systematically review the literature and provide a meta-analysis of the diagnostic accuracy, specificity, sensitivity, and nega...
Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.
Scientific reports
Feb 18, 2025
Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap between the current state of CCE in clinical practice, and the state of its counterpart optical colonosc...
Use of Artificial Intelligence in Lower Gastrointestinal and Small Bowel Disorders: An Update Beyond Polyp Detection.
Journal of clinical gastroenterology
Feb 1, 2025
Machine learning and its specialized forms, such as Artificial Neural Networks and Convolutional Neural Networks, are increasingly being used for detecting and managing gastrointestinal conditions. Recent advancements involve using Artificial Neural ...
S2P-Matching: Self-Supervised Patch-Based Matching Using Transformer for Capsule Endoscopic Images Stitching.
IEEE transactions on bio-medical engineering
Jan 21, 2025
The Magnetically Controlled Capsule Endoscopy (MCCE) has a limited shooting range, resulting in capturing numerous fragmented images and an inability to precisely locate and examine the region of interest (ROI) as traditional endoscopy can. Addressin...
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