Artificial Intelligence in Capsule Endoscopy

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. Provides the current and developing practical application of AI tools in capsule endoscopy Explains the disruptive nature of AI tools in capsule endoscopy video analysis to provide a better perspective on how AI will change the landscape of capsule endoscopy practice in the future Includes specific lesion detection, delivering key summaries and practical applications regarding AI tools use Bridges theorical foundations and practical utility of AI in capsule endoscopy

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. Provides the current and developing practical application of AI tools in capsule endoscopy Explains the disruptive nature of AI tools in capsule endoscopy video analysis to provide a better perspective on how AI will change the landscape of capsule endoscopy practice in the future Includes specific lesion detection, delivering key summaries and practical applications regarding AI tools use Bridges theorical foundations and practical utility of AI in capsule endoscopy