Abstract
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.