ReviewNature reviews. Gastroenterology & hepatology2015
Software for enhanced video capsule endoscopy: challenges for essential progress.
Review in Nature reviews. Gastroenterology & hepatology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
48 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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 · 2025Pooled it
- Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Expected Values of Artificial Intelligence in Colon Capsule Endoscopy: Artificial Intelligence in Capsule Endoscopy Consortium Position Statement.Endoscopy international open · 2026Article
- Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.Scientific reports · 2025Article
- Galar - a large multi-label video capsule endoscopy dataset.Scientific data · 2025Article
- Robotic wireless capsule endoscopy: recent advances and upcoming technologies.Nature communications · 2024Review
- Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images.Diagnostics (Basel, Switzerland) · 2024Article
- An efficient computational framework for gastrointestinal disorder prediction using attention-based transfer learning.PeerJ. Computer science · 2024Article
- Enteroscopy versus Video Capsule Endoscopy for Automatic Diagnosis of Small Bowel Disorders-A Comparative Analysis of Artificial Intelligence Applications.Biomedicines · 2023Review
- Capsule Enteroscopy Using the MirocamLife (Basel, Switzerland) · 2023Article
- Study on image data cleaning method of early esophageal cancer based on VGG_NIN neural network.Scientific reports · 2022Article
- Development and Validation of an Artificial Intelligence Model for Small Bowel Capsule Endoscopy Video Review.JAMA network open · 2022Article
- Soft hybrid intrinsically motile robot for wireless small bowel enteroscopy.Surgical endoscopy · 2022Article
- Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- 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 · 2021Article
- Artificial intelligence and colon capsule endoscopy: automatic detection of blood in colon capsule endoscopy using a convolutional neural network.Endoscopy international open · 2021Article
- Role of Artificial Intelligence in Video Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
- Kvasir-Capsule, a video capsule endoscopy dataset.Scientific data · 2021Article
- Deep Transfer Learning for Automated Intestinal Bleeding Detection in Capsule Endoscopy Imaging.Journal of digital imaging · 2021Article
- Automated bleeding detection in wireless capsule endoscopy images based on color feature extraction from Gaussian mixture model superpixels.Medical & biological engineering & computing · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Video capsule endoscopy (VCE) has revolutionized the diagnostic work-up in the field of small bowel diseases. Furthermore, VCE has the potential to become the leading screening technique for the entire gastrointestinal tract. Computational methods that can be implemented in software can enhance the diagnostic yield of VCE both in terms of efficiency and diagnostic accuracy. Since the appearance of the first capsule endoscope in clinical practice in 2001, information technology (IT) research groups have proposed a variety of such methods, including algorithms for detecting haemorrhage and lesions, reducing the reviewing time, localizing the capsule or lesion, assessing intestinal motility, enhancing the video quality and managing the data. Even though research is prolific (as measured by publication activity), the progress made during the past 5 years can only be considered as marginal with respect to clinically significant outcomes. One thing is clear-parallel pathways of medical and IT scientists exist, each publishing in their own area, but where do these research pathways meet? Could the proposed IT plans have any clinical effect and do clinicians really understand the limitations of VCE software? In this Review, we present an in-depth critical analysis that aims to inspire and align the agendas of the two scientific groups.
Indexed as
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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.