ArticleBMJ open gastroenterology2021
Deep learning and capsule endoscopy: automatic identification and differentiation of small bowel lesions with distinct haemorrhagic potential using a convolutional neural network.
Article in BMJ open gastroenterology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Artificial Intelligence in Gastrointestinal Wireless Capsule Endoscopy: A Systematic Literature Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Recent technological advances in video capsule endoscopy: a comprehensive review.Clinical endoscopy · 2026Review
- Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis.Acute and critical care · 2026Article
- Advancing Medical Decision-Making with AI: A Comprehensive Exploration of the Evolution from Convolutional Neural Networks to Capsule Networks.Journal of imaging · 2025Review
- Review
- Artificial intelligence-assisted versus conventional reading in pan-intestinal capsule endoscopy for suspected mid-lower gastrointestinal bleeding: a retrospective analysis of a prospective cohort.BMJ open gastroenterology · 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
- Deep learning and capsule endoscopy: automatic panendoscopic detection of protruding lesions.BMJ open gastroenterology · 2025Article
- Utilizing Deep Convolutional Neural Networks and Hybrid Classification for Gastrointestinal Disease Diagnosis from Capsule Endoscopy Images.Journal of biomedical physics & engineering · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Unmet Needs of Artificial Intelligence in Small Bowel Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology.Journal of clinical medicine · 2025Article
- Deep Learning and Minimally Invasive Endoscopy: Panendoscopic Detection of Pleomorphic Lesions.GE Portuguese journal of gastroenterology · 2024Article
- Capsule endoscopy and panendoscopy: A journey to the future of gastrointestinal endoscopy.World journal of gastroenterology · 2024Article
- Cloud technology and capsule endoscopy: A single-center users' experience of remote online video analysis and reporting.Endoscopy international open · 2024Article
- From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?Diagnostics (Basel, Switzerland) · 2024Review
- Automatic detection of small bowel lesions with different bleeding risks based on deep learning models.World journal of gastroenterology · 2024Article
- The Future of Minimally Invasive Capsule Panendoscopy: Robotic Precision, Wireless Imaging and AI-Driven Insights.Cancers · 2023Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveCapsule endoscopy (CE) is pivotal for evaluation of small bowel disease. Obscure gastrointestinal bleeding most often originates from the small bowel. CE frequently identifies a wide range of lesions with different bleeding potentials in these patients. However, reading CE examinations is a time-consuming task. Convolutional neural networks (CNNs) are highly efficient artificial intelligence tools for image analysis. This study aims to develop a CNN-based model for identification and differentiation of multiple small bowel lesions with distinct haemorrhagic potential using CE images.
designWe developed, trained, and validated a denary CNN based on CE images. Each frame was labelled according to the type of lesion (lymphangiectasia, xanthomas, ulcers, erosions, vascular lesions, protruding lesions, and blood). The haemorrhagic potential was assessed by Saurin's classification. The entire dataset was divided into training and validation sets. The performance of the CNN was measured by the area under the receiving operating characteristic curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).
resultsA total of 53 555 CE images were included. The model had an overall accuracy of 99%, a sensitivity of 88%, a specificity of 99%, a PPV of 87%, and an NPV of 99% for detection of multiple small bowel abnormalities and respective classification of bleeding potential.
conclusionWe developed and tested a CNN-based model for automatic detection of multiple types of small bowel lesions and classification of the respective bleeding potential. This system may improve the diagnostic yield of CE for these lesions and overall CE efficiency.
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