ArticleEndoscopy2024
Direct comparison of multiple computer-aided polyp detection systems.
Article in Endoscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Comparative Performance of Artificial Intelligence-Based Computer-Aided Detection Systems for Colorectal Polyps: A Systematic Review and Network Meta-Analysis.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026Pooled it
- Comparative bibliometric analysis of artificial intelligence-assisted polyp diagnosis and AI-assisted digestive endoscopy: trends and growth in AI gastroenterology (2003-2023).Frontiers in medicine · 2024Pooled it
- Trial
- A prospective study evaluating an artificial intelligence-based system for withdrawal time measurement.Endoscopy · 2026Trial
- Gastrointestinal endoscopic image style transfer using EndoStyle to improve artificial intelligence prediction models.NPJ digital medicine · 2026Article
- Artificial intelligence assisted colorectal lesion detection in private practices a randomized controlled study.NPJ digital medicine · 2026Article
- Gastrointestinal polyp detection method based on the improved RT-DETR.Scientific reports · 2026Article
- [Risks and benefits of artificial intelligence in luminal endoscopy].Innere Medizin (Heidelberg, Germany) · 2026Review
- Computer-assisted detection of colorectal polyps: a narrative review of clinical utility, ongoing limitations, and opportunities for advancement.Translational gastroenterology and hepatology · 2026Review
- Automated endoscopic image quality assessment for robust colorectal lesion detection.BMC medical imaging · 2025Article
- Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes.World journal of gastroenterology · 2025Review
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.Scientific data · 2025Article
- A comparative study benchmarking colon polyp with computer-aided detection (CADe) software.DEN open · 2025Article
- A prospective comparison of two computer aided detection systems with different false positive rates in colonoscopy.NPJ digital medicine · 2024Article
- Comparing Raman Spectroscopy-Based Artificial Intelligence to High-Definition White Light Endoscopy for Endoscopic Diagnosis of Gastric Neoplasia: A Feasibility Proof-of-Concept Study.Diagnostics (Basel, Switzerland) · 2024Article
- CRH-YOLO for precise and efficient detection of gastrointestinal polyps.Scientific reports · 2024Article
- Understanding the discrepancy in the effectiveness of artificial intelligence-assisted colonoscopy: from randomized controlled trials to clinical reality.Clinical endoscopy · 2024Article
- Impact of User's Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection.Gut and liver · 2024Article
- Advancements in Barrett's esophagus detection: The role of artificial intelligence and its implications.World journal of gastroenterology · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND AND STUDY
aimsArtificial intelligence (AI)-based systems for computer-aided detection (CADe) of polyps receive regular updates and occasionally offer customizable detection thresholds, both of which impact their performance, but little is known about these effects. This study aimed to compare the performance of different CADe systems on the same benchmark dataset.
methods101 colonoscopy videos were used as benchmark. Each video frame with a visible polyp was manually annotated with bounding boxes, resulting in 129 705 polyp images. The videos were then analyzed by three different CADe systems, representing five conditions: two versions of GI Genius, Endo-AID with detection Types A and B, and EndoMind, a freely available system. Evaluation included an analysis of sensitivity and false-positive rate, among other metrics.
resultsEndo-AID detection Type A, the earlier version of GI Genius, and EndoMind detected all 93 polyps. Both the later version of GI Genius and Endo-AID Type B missed 1 polyp. The mean per-frame sensitivities were 50.63 % and 67.85 %, respectively, for the earlier and later versions of GI Genius, 65.60 % and 52.95 %, respectively, for Endo-AID Types A and B, and 60.22 % for EndoMind.
conclusionsThis study compares the performance of different CADe systems, different updates, and different configuration modes. This might help clinicians to select the most appropriate system for their specific needs.
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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.