TY - JOUR
T1 - Artificial intelligence in capsule endoscopy
T2 - A practical guide to its past and future challenges
AU - Kim, Sang Hoon
AU - Lim, Yun Jeong
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Computer-aided reading
KW - Convolutional neural network
KW - Small bowel imaging
KW - Wireless capsule endoscopy
UR - http://www.scopus.com/inward/record.url?scp=85116633732&partnerID=8YFLogxK
U2 - 10.3390/diagnostics11091722
DO - 10.3390/diagnostics11091722
M3 - Review article
AN - SCOPUS:85116633732
SN - 2075-4418
VL - 11
JO - Diagnostics
JF - Diagnostics
IS - 9
M1 - 1722
ER -