ArticleEndoscopy international open2017
KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes.
Article in Endoscopy international open, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- eNCApsulate: neural cellular automata for precision diagnosis on capsule endoscopes.International journal of computer assisted radiology and surgery · 2026Article
- VIM-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression.Scientific data · 2025Article
- Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.Scientific reports · 2025Article
- Utilizing Deep Convolutional Neural Networks and Hybrid Classification for Gastrointestinal Disease Diagnosis from Capsule Endoscopy Images.Journal of biomedical physics & engineering · 2025Article
- Advancing Colorectal Cancer Diagnostics from Barium Enema to AI-Assisted Colonoscopy.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Polypoid Lesion Segmentation Using YOLO-V8 Network in Wireless Video Capsule Endoscopy Images.Diagnostics (Basel, Switzerland) · 2024Article
- Gastrointestinal tract disease detection via deep learning based structural and statistical features optimized hexa-classification model.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model.Biomedical engineering online · 2023Article
- Public Imaging Datasets of Gastrointestinal Endoscopy for Artificial Intelligence: a Review.Journal of digital imaging · 2023Review
- E pluribus unum interpretable convolutional neural networks.Scientific reports · 2023Article
- A multi-centre polyp detection and segmentation dataset for generalisability assessment.Scientific data · 2023Article
- Rhode Island gastroenterology video capsule endoscopy data set.Scientific data · 2022Article
- Development and Validation of an Artificial Intelligence Model for Small Bowel Capsule Endoscopy Video Review.JAMA network open · 2022Article
- Surgical data science - from concepts toward clinical translation.Medical image analysis · 2022Review
- Towards a better understanding of annotation tools for medical imaging: a survey.Multimedia tools and applications · 2022Article
- Artificial intelligence, capsule endoscopy, databases, and the Sword of Damocles.Endoscopy international open · 2021Article
- Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges.Diagnostics (Basel, Switzerland) · 2021Review
- Role of Artificial Intelligence in Video Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
- Kvasir-Capsule, a video capsule endoscopy dataset.Scientific data · 2021Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsCapsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE.
methodsOpen-source software was used for the KID database. Clinicians contribute anonymized, annotated CE images and videos. Graphic annotations are supported by an open-access annotation tool (Ratsnake). We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA. The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers.
resultsThe MLA performed best in measuring lymphangiectasias with a JI of 81 ± 6 %. The other lesion types were: angioectasias (JI 64 ± 11 %), aphthae (JI 64 ± 8 %), chylous cysts (JI 70 ± 14 %), polypoid lesions (JI 75 ± 21 %), and ulcers (JI 56 ± 9 %).
conclusionMLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL). Automated lesion measurement is therefore feasible. KID is currently the only open-source CE database developed specifically to aid development of MDSS. Our experiment demonstrates this potential.
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Registered trials
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