Artificial Intelligence in Capsule Endoscopy: A Gamechanger for a Groundbreaking Technique highlights the importance of Artificial Intelligence (AI) application in capsule endoscopy. AI will have a key role in the mid/long-term for gastrointestinal endoscopy and capsule endoscopy. This field is a prime area for the use of AI tools with over 50,000 images per endoscopy capsule video, making video analysis a time and resource consuming task and prone to error. With the application of AI image analysis tools (primarily Convolutional Neural Networks) we can decrease capsule endoscopy video reading time and resources and greatly benefit diagnostic accuracy and patient outcomes.
In 15 chapters, this important reference provides a global and comprehensive perspective from the background information of AI, machine learning, deep learning and their implications in GI endoscopy. It showcases AI practical use in lesion detection and in relevant clinical indications (like obscure gastrointestinal bleeding and inflammatory bowel disease), and points to future applications of AI within the field.
1. Artificial Intelligence in Capsule Endoscopy - p. i 2. Cover - p. i 3. Title Page - p. ii 4. Table of Contents - p. iv 5. Copyright - p. iii 6. List of Contributors - p. xii 7. Preface - p. xvi 8. Acknowledgments - p. xvii 9. Chapter 1 Artificial intelligence: machine learning, deep learning, and applications in gastrointestinal endoscopy - p. 1 10. Chapter 2 Wireless capsule endoscopy: concept and modalities - p. 11 11. Chapter 3 Capsule endoscopy: wide clinical scope - p. 21 12. Chapter 4 The role of capsule endoscopy in diagnosis and clinical management of obscure gastrointestinal bleeding - p. 53 13. Chapter 5 The role of capsule endoscopy in diagnosis and clinical management of inflammatory bowel disease - p. 69 14. Chapter 6 Artificial intelligence for automatic detection of blood and hematic residues - p. 91 15. Chapter 7 Artificial intelligence in capsule endoscopy for detection of ulcers and erosions - p. 101 16. Chapter 8 Artificial intelligence for protruding lesions - p. 121 17. Chapter 9 Artificial intelligence for vascular lesions - p. 149 18. Chapter 10 Artificial intelligence for luminal content analysis and miscellaneous findings - p. 163 19. Chapter 11 Small bowel and colon cleansing in capsule endoscopy - p. 181 20. Chapter 12 Introducing blockchain technology in data storage to foster big data and artificial intelligence applications in healthcare systems - p. 199 21. Chapter 13 Magnetic capsule endoscopy: concept and application of artificial intelligence - p. 217 22. Chapter 14 Nonwhite light endoscopy in capsule endoscopy: Fujinon Intelligent Chromo Endoscopy and blue mode - p. 243 23. Chapter 15 Colon capsule endoscopy and artificial intelligence: a perfect match for panendoscopy - p. 255 24. Index - p. 271
Artificial Intelligence in Capsule Endoscopy: A Gamechanger for a Groundbreaking Technique highlights the importance of Artificial Intelligence (AI) application in capsule endoscopy. AI will have a key role in the mid/long-term for gastrointestinal endoscopy and capsule endoscopy. This field is a prime area for the use of AI tools with over 50,000 images per endoscopy capsule video, making video analysis a time and resource consuming task and prone to error. With the application of AI image analysis tools (primarily Convolutional Neural Networks) we can decrease capsule endoscopy video reading time and resources and greatly benefit diagnostic accuracy and patient outcomes.
In 15 chapters, this important reference provides a global and comprehensive perspective from the background information of AI, machine learning, deep learning and their implications in GI endoscopy. It showcases AI practical use in lesion detection and in relevant clinical indications (like obscure gastrointestinal bleeding and inflammatory bowel disease), and points to future applications of AI within the field.
1. Artificial Intelligence in Capsule Endoscopy - p. i 2. Cover - p. i 3. Title Page - p. ii 4. Table of Contents - p. iv 5. Copyright - p. iii 6. List of Contributors - p. xii 7. Preface - p. xvi 8. Acknowledgments - p. xvii 9. Chapter 1 Artificial intelligence: machine learning, deep learning, and applications in gastrointestinal endoscopy - p. 1 10. Chapter 2 Wireless capsule endoscopy: concept and modalities - p. 11 11. Chapter 3 Capsule endoscopy: wide clinical scope - p. 21 12. Chapter 4 The role of capsule endoscopy in diagnosis and clinical management of obscure gastrointestinal bleeding - p. 53 13. Chapter 5 The role of capsule endoscopy in diagnosis and clinical management of inflammatory bowel disease - p. 69 14. Chapter 6 Artificial intelligence for automatic detection of blood and hematic residues - p. 91 15. Chapter 7 Artificial intelligence in capsule endoscopy for detection of ulcers and erosions - p. 101 16. Chapter 8 Artificial intelligence for protruding lesions - p. 121 17. Chapter 9 Artificial intelligence for vascular lesions - p. 149 18. Chapter 10 Artificial intelligence for luminal content analysis and miscellaneous findings - p. 163 19. Chapter 11 Small bowel and colon cleansing in capsule endoscopy - p. 181 20. Chapter 12 Introducing blockchain technology in data storage to foster big data and artificial intelligence applications in healthcare systems - p. 199 21. Chapter 13 Magnetic capsule endoscopy: concept and application of artificial intelligence - p. 217 22. Chapter 14 Nonwhite light endoscopy in capsule endoscopy: Fujinon Intelligent Chromo Endoscopy and blue mode - p. 243 23. Chapter 15 Colon capsule endoscopy and artificial intelligence: a perfect match for panendoscopy - p. 255 24. Index - p. 271
