ReviewClinical endoscopy2019
Recent Development of Computer Vision Technology to Improve Capsule Endoscopy.
Review in Clinical endoscopy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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.
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Who cites it
14 citing papers in PubMed.
- Recent technological advances in video capsule endoscopy: a comprehensive review.Clinical endoscopy · 2026Review
- Galar - a large multi-label video capsule endoscopy dataset.Scientific data · 2025Article
- Article
- Development and Validation of a Multi-Task Artificial Intelligence-Assisted System for Small Bowel Capsule Endoscopy.International journal of general medicine · 2025Article
- Revealing the Boundaries of Selected Gastro-Intestinal (GI) Organs by Implementing CNNs in Endoscopic Capsule Images.Diagnostics (Basel, Switzerland) · 2023Article
- A Novel Computer-Aided Detection/Diagnosis System for Detection and Classification of Polyps in Colonoscopy.Diagnostics (Basel, Switzerland) · 2023Article
- Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering.Diagnostics (Basel, Switzerland) · 2022Article
- Review: Colon Capsule Endoscopy in Inflammatory Bowel Disease.Diagnostics (Basel, Switzerland) · 2022Review
- Efficacy of a comprehensive binary classification model using a deep convolutional neural network for wireless capsule endoscopy.Scientific reports · 2021Article
- Role of Artificial Intelligence in Video Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
- Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality.Diagnostics (Basel, Switzerland) · 2021Article
- Kvasir-Capsule, a video capsule endoscopy dataset.Scientific data · 2021Article
- Development of a deep learning-based software for calculating cleansing score in small bowel capsule endoscopy.Scientific reports · 2021Article
- Artificial intelligence that determines the clinical significance of capsule endoscopy images can increase the efficiency of reading.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Capsule endoscopy (CE) is a preferred diagnostic method for analyzing small bowel diseases. However, capsule endoscopes capture a sparse number of images because of their mechanical limitations. Post-procedural management using computational methods can enhance image quality. Additional information, including depth, can be obtained by using recently developed computer vision techniques. It is possible to measure the size of lesions and track the trajectory of capsule endoscopes using the computer vision technology, without requiring additional equipment. Moreover, the computational analysis of CE images can help detect lesions more accurately within a shorter time. Newly introduced deep leaning-based methods have shown more remarkable results over traditional computerized approaches. A large-scale standard dataset should be prepared to develop an optimal algorithms for improving the diagnostic yield of CE. The close collaboration between information technology and medical professionals is needed.
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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.