ArticleDiagnostics (Basel, Switzerland)2024
Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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.
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Who cites it
10 citing papers in PubMed, 35 citations in OpenAlex.
- Artificial Intelligence in Gastrointestinal Wireless Capsule Endoscopy: A Systematic Literature Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2026Review
- Automated surgical instrument recognition in laparoscopic cholecystectomy videos: a novel two-step deep learning approach with virtual image synthesis.Surgical endoscopy · 2026Article
- A deep learning approach to predict the results of root coverage procedures based on intraoral photographs: a retrospective study.BMC oral health · 2025Article
- Beyond Human Vision: Revolutionizing the Localization of Diminutive Sessile Polyps in Colonoscopy.Bioengineering (Basel, Switzerland) · 2025Article
- Small Object Detection in Agriculture: A Case Study on Durian Orchards Using EN-YOLO and Thermal Fusion.Plants (Basel, Switzerland) · 2025Article
- Deep Learning for Comprehensive Analysis of Retinal Fundus Images: Detection of Systemic and Ocular Conditions.Bioengineering (Basel, Switzerland) · 2025Article
- Deep learning model applied to real-time delineation of colorectal polyps.BMC medical informatics and decision making · 2025Article
- Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights.Diagnostics (Basel, Switzerland) · 2024Article
- Automatic lesion detection for narrow-band imaging bronchoscopy.Journal of medical imaging (Bellingham, Wash.) · 2024Article
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastrointestinal (GI) tract disorders are a significant public health issue. They are becoming more common and can cause serious health problems and high healthcare costs. Small bowel tumours (SBTs) and colorectal cancer (CRC) are both becoming more prevalent, especially among younger adults. Early detection and removal of polyps (precursors of malignancy) is essential for prevention. Wireless Capsule Endoscopy (WCE) is a procedure that utilises swallowable camera devices that capture images of the GI tract. Because WCE generates a large number of images, automated polyp segmentation is crucial. This paper reviews computer-aided approaches to polyp detection using WCE imagery and evaluates them using a dataset of labelled anomalies and findings. The study focuses on YOLO-V8, an improved deep learning model, for polyp segmentation and finds that it performs better than existing methods, achieving high precision and recall. The present study underscores the potential of automated detection systems in improving GI polyp identification.
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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.