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

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초록

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.

키워드

artificial intelligencewireless capsule endoscopyconvolutional neural networkcomputer-aided readingsmall bowel imagingCLASSIFICATIONSEGMENTATIONRECOGNITIONLESIONSMODEL
제목
Artificial Intelligence in Capsule Endoscopy: A Practical Guide to Its Past and Future Challenges
저자
Kim, Sang HoonLim, Yun Jeong
DOI
10.3390/diagnostics11091722
발행일
2021-09
유형
Review
저널명
DIAGNOSTICS
11
9