ArticleScientific reports2019
Computer-aided diagnosis of colorectal polyps using linked color imaging colonoscopy to predict histology.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04749277 (Artificial Intelligence in the Characterization of Small and Diminutive Colorectal Polyps), which is not on this map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
Artificial Intelligence in the Characterization of Small and Diminutive Colorectal Polyps: A Prospective Study in a Clinical Setting Using CAD EYE®
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 69 citations in OpenAlex.
- Computer-aided diagnosis for colorectal polyp in comparison with endoscopists: Systematic review and meta-analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2025Pooled it
- Deep ensemble learning for gastrointestinal diagnosis using endoscopic image classification.PeerJ. Computer science · 2025Article
- Mucosal imaging in colon polyps: New advances and what the future may hold.World journal of gastroenterology · 2022Review
- Hybrid and Deep Learning Approach for Early Diagnosis of Lower Gastrointestinal Diseases.Sensors (Basel, Switzerland) · 2022Article
- Current Status and Future Perspectives of Artificial Intelligence in Colonoscopy.Journal of clinical medicine · 2022Review
- Artificial Intelligence in Endoscopy.Digestive diseases and sciences · 2022Review
- Artificial intelligence-assisted detection and classification of colorectal polyps under colonoscopy: a systematic review and meta-analysis.Annals of translational medicine · 2021Article
- Algorithm combining virtual chromoendoscopy features for colorectal polyp classification.Endoscopy international open · 2021Article
- Kvasir-Capsule, a video capsule endoscopy dataset.Scientific data · 2021Article
- Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era.Current oncology (Toronto, Ont.) · 2021Review
- Scope of Artificial Intelligence in Screening and Diagnosis of Colorectal Cancer.Journal of clinical medicine · 2020Review
- Potential applications of artificial intelligence in colorectal polyps and cancer: Recent advances and prospects.World journal of gastroenterology · 2020Review
- HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.Scientific data · 2020Article
- Artificial Intelligence in Gastrointestinal Endoscopy.Clinical endoscopy · 2020Article
- RGB Pixel Brightness Characteristics of Linked Color Imaging in Early Gastric Cancer: A Pilot Study.Gastroenterology research and practice · 2020Article
- Artificial inelegance in endoscopy: An updated auricle of Delphi!Saudi journal of gastroenterology : official journal of the Saudi Gastroenterology AssociationArticle
- Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy.Therapeutic advances in gastrointestinal endoscopyReview
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed a computer-aided diagnosis (CAD) system based on linked color imaging (LCI) images to predict the histological results of polyps by analyzing the colors of the lesions. A total of 139 images of adenomatous polyps and 69 images of non-adenomatous polyps obtained from our hospital were collected and used to train the CAD system. A test set of LCI images, including both adenomatous and non-adenomatous polyps, was prospectively collected from patients who underwent colonoscopies between Oct and Dec 2017; this test set was used to assess the diagnostic abilities of the CAD system compared to those of human endoscopists (two experts and two novices). The accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of this novel CAD system for the training set were 87.0%, 87.1%, 87.0%, 93.1%, and 76.9%, respectively. The test set included 115 adenomatous polyps and 66 non-adenomatous polyps that were prospectively collected. The CAD system identified adenomatous or non-adenomatous polyps in the test set with an accuracy of 78.4%, a sensitivity of 83.3%, a specificity of 70.1%, a PPV of 82.6%, and an NPV of 71.2%. The accuracy of the CAD system was comparable to that of the expert endoscopists (78.4% vs 79.6%; p = 0.517). In addition, the diagnostic accuracy of the novices was significantly lower to the performance of the experts (70.7% vs 79.6%; p = 0.018). A novel CAD system based on LCI could be a rapid and powerful decision-making tool for endoscopists.
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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.