ArticleDiagnostics (Basel, Switzerland)2022
Performance of a Deep Learning System for Automatic Diagnosis of Protruding Lesions in Colon Capsule Endoscopy.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 17 citations in OpenAlex.
- Artificial Intelligence in Gastrointestinal Endoscopy and Hemostatic Decision-Making: Current Evidence, Clinical Implications and Implementation Barriers.Life (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Gastrointestinal Wireless Capsule Endoscopy: A Systematic Literature Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Expected Values of Artificial Intelligence in Colon Capsule Endoscopy: Artificial Intelligence in Capsule Endoscopy Consortium Position Statement.Endoscopy international open · 2026Article
- Application of Explainable Artificial Intelligence Based on Visual Explanation in Digestive Endoscopy.Bioengineering (Basel, Switzerland) · 2025Review
- Deep learning and capsule endoscopy: automatic panendoscopic detection of protruding lesions.BMJ open gastroenterology · 2025Article
- Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.Journal of clinical medicine · 2025Review
- Capsule robots for the monitoring, diagnosis, and treatment of intestinal diseases.Materials today. Bio · 2024Review
- Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications.Diagnostics (Basel, Switzerland) · 2024Review
- A Comprehensive Review of Artificial Intelligence and Colon Capsule Endoscopy: Opportunities and Challenges.Diagnostics (Basel, Switzerland) · 2024Review
- Capsule endoscopy and panendoscopy: A journey to the future of gastrointestinal endoscopy.World journal of gastroenterology · 2024Article
- Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care.Diagnostics (Basel, Switzerland) · 2023Review
- The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents.Medicina (Kaunas, Lithuania) · 2023Review
- Clinicians' Guide to Artificial Intelligence in Colon Capsule Endoscopy-Technology Made Simple.Diagnostics (Basel, Switzerland) · 2023Review
- The role of deep learning in diagnosing colorectal cancer.Przeglad gastroenterologiczny · 2023Review
- Diagnosis of Inflammatory Bowel Disease and Colorectal Cancer through Multi-View Stacked Generalization Applied on Gut Microbiome Data.Diagnostics (Basel, Switzerland) · 2022Article
- Endoscopic capsule robot-based diagnosis, navigation and localization in the gastrointestinal tract.Frontiers in robotics and AI · 2022Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundColon capsule endoscopy (CCE) is an alternative for patients unwilling or with contraindications for conventional colonoscopy. Colorectal cancer screening may benefit greatly from widespread acceptance of a non-invasive tool such as CCE. However, reviewing CCE exams is a time-consuming process, with risk of overlooking important lesions. We aimed to develop an artificial intelligence (AI) algorithm using a convolutional neural network (CNN) architecture for automatic detection of colonic protruding lesions in CCE images. An anonymized database of CCE images collected from a total of 124 patients was used. This database included images of patients with colonic protruding lesions or patients with normal colonic mucosa or with other pathologic findings. A total of 5715 images were extracted for CNN development. Two image datasets were created and used for training and validation of the CNN. The AUROC for detection of protruding lesions was 0.99. The sensitivity, specificity, PPV and NPV were 90.0%, 99.1%, 98.6% and 93.2%, respectively. The overall accuracy of the network was 95.3%. The developed deep learning algorithm accurately detected protruding lesions in CCE images. The introduction of AI technology to CCE may increase its diagnostic accuracy and acceptance for screening of colorectal neoplasia.
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What OpenQuestion holds
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.