ArticleScientific data2021
Kvasir-Capsule, a video capsule endoscopy dataset.
Article in Scientific data, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
60 citing papers in PubMed, 1 synthesis or guideline pooled it, 250 citations in OpenAlex.
- Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.Frontiers in artificial intelligence · 2026Pooled it
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- Review of Gastrointestinal Capsule Robots: Materials, Actuation, Diagnostics, and Therapeutics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Fabrication of an Anatomically Realistic Intestinal Phantom with Villous Microstructure.Bioengineering (Basel, Switzerland) · 2026Article
- An End-to-End Deep Learning System for Gastrointestinal Bleeding Detection and Quantification in Wireless Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2026Article
- LightGastroFormer: a lightweight multi-resolution transformer for gastrointestinal disease classification.Scientific reports · 2026Article
- A histopathologically verified dataset of magnifying narrow-band imaging endoscopy for classifying the gastric precancerous cascade.Scientific data · 2026Article
- GAD-YOLO: a gastrointestinal abnormality detection YOLO model with multi-scale channel attention and residual fusion.Medical & biological engineering & computing · 2026Article
- Towards generalizable AI in medicine via Generalist-Specialist Collaboration.Nature biomedical engineering · 2026Article
- Artificial Intelligence in Gastrointestinal Wireless Capsule Endoscopy: A Systematic Literature Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- High-precision classification of WCE-based gastrointestinal abnormality using a fusion deep learning approach.Scientific reports · 2026Article
- SIG-CFFNet: Structural Information-Guided Cascaded Feature Fusion Network for Gastrointestinal Anatomy Classification.Annals of biomedical engineering · 2026Article
- eNCApsulate: neural cellular automata for precision diagnosis on capsule endoscopes.International journal of computer assisted radiology and surgery · 2026Article
- Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.Scientific reports · 2025Article
- Combining the Variational and Deep Learning Techniques for Classification of Video Capsule Endoscopic Images.Journal of imaging informatics in medicine · 2025Article
- Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination.World journal of gastroenterology · 2025Review
- Development of deep learning-based narrow-band imaging endocytoscopic classification for predicting colorectal lesions from a retrospective study.Nature communications · 2025Article
- Emulating Hyperspectral and Narrow-Band Imaging for Deep-Learning-Driven Gastrointestinal Disorder Detection in Wireless Capsule Endoscopy.Bioengineering (Basel, Switzerland) · 2025Article
- Integration of Artificial Intelligence-Enhanced Capsule Endoscopy in Clinical Practice: A Review of Market-Available Tools for Clinical Practice.Digestive diseases and sciences · 2025Review
- Gastrointestinal tract disease classification from wireless capsule endoscopy images based on deep learning information fusion and Newton Raphson controlled marine predator algorithm.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
22 authors at 11 institutions in 2 countries.
Funding
Abstract
Artificial intelligence (AI) is predicted to have profound effects on the future of video capsule endoscopy (VCE) technology. The potential lies in improving anomaly detection while reducing manual labour. Existing work demonstrates the promising benefits of AI-based computer-assisted diagnosis systems for VCE. They also show great potential for improvements to achieve even better results. Also, medical data is often sparse and unavailable to the research community, and qualified medical personnel rarely have time for the tedious labelling work. We present Kvasir-Capsule, a large VCE dataset collected from examinations at a Norwegian Hospital. Kvasir-Capsule consists of 117 videos which can be used to extract a total of 4,741,504 image frames. We have labelled and medically verified 47,238 frames with a bounding box around findings from 14 different classes. In addition to these labelled images, there are 4,694,266 unlabelled frames included in the dataset. The Kvasir-Capsule dataset can play a valuable role in developing better algorithms in order to reach true potential of VCE technology.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.