ArticleGut and liver2023
Capsule Endoscopy in Inflammatory Bowel Disease: Panenteric Capsule Endoscopy and Application of Artificial Intelligence.
Article in Gut and liver, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 11 citations in OpenAlex.
- Synthetic data generation: challenges and perspectives for gastrointestinal medicine.Nature reviews. Gastroenterology & hepatology · 2026Review
- Current approaches to disease severity and therapy effectiveness assessment in patients with inflammatory bowel disease.World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Pan-enteric Capsule Endoscopy in Crohn's Disease: Impact on Therapeutic Decisions and Inter-observer Agreement in a Multicentre Case-based Study.Endoscopy international open · 2026Article
- Inter-Rater Disagreements in Applying the Montreal Classification for Crohn's Disease: The Five-Nations Survey Study.United European gastroenterology journal · 2025Article
- Diagnostic Methods and Biomarkers in Inflammatory Bowel Disease.Diagnostics (Basel, Switzerland) · 2025Review
- The Future of Minimally Invasive GI and Capsule Diagnostics (REFLECT), October 2024.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence in inflammatory bowel disease endoscopy - a review of current evidence and a critical perspective on future challenges.Therapeutic advances in gastroenterology · 2025Review
- Article
- Current Approaches for Monitoring of Patients with Inflammatory Bowel Diseases: A Narrative Review.Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn's disease (CD). In 2017, the panenteric capsule (PillCam Crohn's system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.
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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.