Summary of Artificial Intelligence in Gastrointestinal Bleeding Analysis For Video Capsule Endoscopy: Insights, Innovations, and Prospects (2008-2023), by Tanisha Singh et al.
Artificial Intelligence in Gastrointestinal Bleeding Analysis for Video Capsule Endoscopy: Insights, Innovations, and Prospects (2008-2023)
by Tanisha Singh, Shreshtha Jha, Nidhi Bhatt, Palak Handa, Nidhi Goel, Sreedevi Indu
First submitted to arxiv on: 1 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study highlights the pressing need for improved diagnostic strategies for gastrointestinal bleeding, which affects millions worldwide. Current methods are limited, leading to delays and inaccuracies. Video Capsule Endoscopy (VCE) has shown promise, but its effectiveness is hindered by manual analysis and human error. To address these limitations, machine learning (ML) techniques can be applied to automate GI bleeding detection within VCE. This review analyzes 113 papers published between 2008 and 2023, evaluating ML methodologies in bleeding detection, their challenges, and potential directions. The study explores AI techniques in VCE frame analysis, including open-source datasets, mathematical performance metrics, and technique categorization. By overcoming existing challenges, this research sets a foundation for future advancements in gastrointestinal diagnostics through interdisciplinary collaboration and innovation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gastrointestinal bleeding is a serious problem that affects many people worldwide. Current methods to diagnose this condition are not very good, which can lead to delays and bad outcomes. Video Capsule Endoscopy (VCE) is a new technology that shows the inside of the digestive system, but it’s hard to analyze the images correctly. Machine learning (ML) is a type of artificial intelligence that can help make diagnoses faster and more accurate. This review looks at many studies on ML for GI bleeding detection and identifies what works well and what doesn’t. The study also explores how AI can be used to improve VCE image analysis. By improving our understanding of this technology, we can develop better ways to diagnose and treat gastrointestinal bleeding. |
Keywords
» Artificial intelligence » Machine learning
