TY - JOUR
T1 - Capsule Endoscopy in Inflammatory Bowel Disease
T2 - Panenteric Capsule Endoscopy and Application of Artificial Intelligence
AU - Ukashi, Offir
AU - Soffer, Shelly
AU - Klang, Eyal
AU - Eliakim, Rami
AU - Ben-Horin, Shomron
AU - Kopylov, Uri
N1 - Publisher Copyright:
© Gut and Liver.
PY - 2023/7
Y1 - 2023/7
N2 - Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn’s disease (CD). In 2017, the panenteric capsule (PillCam Crohn’s system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.
AB - Video capsule endoscopy (VCE) of the small-bowel has been proven to accurately diagnose small-bowel inflammation and to predict future clinical flares among patients with Crohn’s disease (CD). In 2017, the panenteric capsule (PillCam Crohn’s system) was introduced for the first time, enabling a reliable evaluation of the whole small and large intestines. The great advantage of visualization of both parts of the gastrointestinal tract in a feasible and single procedure, holds a significant promise for patients with CD, enabling determination of the disease extent and severity, and potentially optimize disease management. In recent years, applications of machine learning, for VCE have been well studied, demonstrating impressive performance and high accuracy for the detection of various gastrointestinal pathologies, among them inflammatory bowel disease lesions. The use of artificial neural network models has been proven to accurately detect/classify and grade CD lesions, and shorten the VCE reading time, resulting in a less tedious process with a potential to minimize missed diagnosis and better predict clinical outcomes. Nevertheless, prospective, and real-world studies are essential to precisely examine artificial intelligence applications in real-life inflammatory bowel disease practice.
KW - Artificial intelligence
KW - Crohn disease
KW - Pan-enteric capsule
KW - Video capsule endoscopy
UR - http://www.scopus.com/inward/record.url?scp=85164845231&partnerID=8YFLogxK
U2 - 10.5009/gnl220507
DO - 10.5009/gnl220507
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C2 - 37305947
AN - SCOPUS:85164845231
SN - 1976-2283
VL - 17
SP - 516
EP - 528
JO - Gut and Liver
JF - Gut and Liver
IS - 4
ER -