ArticleFrontiers in medicine2022
Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Expected Values of Artificial Intelligence in Colon Capsule Endoscopy: Artificial Intelligence in Capsule Endoscopy Consortium Position Statement.Endoscopy international open · 2026Article
- Deep learning and capsule endoscopy: automatic panendoscopic detection of protruding lesions.BMJ open gastroenterology · 2025Article
- AI-Assisted Evaluation of Colon Cleanliness in Capsule Endoscopy Videos.Diagnostics (Basel, Switzerland) · 2025Article
- Capsule Endoscopy: Current Trends, Technological Advancements, and Future Perspectives in Gastrointestinal Diagnostics.Bioengineering (Basel, Switzerland) · 2025Review
- A Comprehensive Review of Artificial Intelligence and Colon Capsule Endoscopy: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2024Review
- Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine.Cancers · 2024Review
- Article
- Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images.Scientific reports · 2023Article
- Artificial intelligence in gastrointestinal endoscopy: a comprehensive review.Annals of gastroenterologyReview
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%.
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Registered trials
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