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Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges.

Sang Hoon KimYun Jeong Lim
Published in: Diagnostics (Basel, Switzerland) (2021)
Artificial intelligence (AI) has revolutionized the medical diagnostic process of various diseases. Since the manual reading of capsule endoscopy videos is a time-intensive, error-prone process, computerized algorithms have been introduced to automate this process. Over the past decade, the evolution of convolutional neural network (CNN) enabled AI to detect multiple lesions simultaneously with increasing accuracy and sensitivity. Difficulty in validating CNN performance and unique characteristics of capsule endoscopy images make computer-aided reading systems in capsule endoscopy still on a preclinical level. Although AI technology can be used as an auxiliary second observer in capsule endoscopy, it is expected that in the near future, it will effectively reduce the reading time and ultimately become an independent, integrated reading system.
Keyphrases
  • artificial intelligence
  • deep learning
  • convolutional neural network
  • machine learning
  • small bowel
  • big data
  • working memory
  • healthcare
  • stem cells
  • mesenchymal stem cells
  • clinical decision support
  • bone marrow