The diagnosis of Crohn's Disease (CD) is based on a combination of clinical, biochemical (serological and fecal), endoscopic, radiological, and histological investigations. In the absence of obstructive symptoms or known stenosis, European guidelines recommend to investigate the small intestine using Video Capsule Endoscopy (VCE) if ileocolonoscopy is not decisive. To reduce the reading time of VCE and increase the number of identified lesions during the examination, various artificial intelligence software/tools have been developed in recent decades. This study aims to be the first prospective multicentric real-life trial to evaluate AI-assisted VCE using SmartScan in identifying typical mucosal abnormalities of the small intestine in patients with suspected CD and its ability to reduce reading time while maintaining the same diagnostic yield and diagnostic accuracy of standard reading. The objective of the study is to evaluate the role of AI-assisted VCE using the OMOM SmartScan in detecting typical small bowel inflammatory lesions (i.e. erosions and ulcers) in patients with suspected CD, and comparing AI with standard reading.