Coeliac disease (CD) is an immune-mediated enteropathy leading to small intestinal mucosal atrophy. Diagnosis relies on serology and duodenal biopsies, but it can be complicated by patchy lesions and differential diagnosis with Non-Celiac Enteropathies (NCEs). This multicenter observational study aims to develop and validate an Artificial Intelligence (AI) system to detect and characterize small bowel mucosal atrophy and other pathological findings using endoscopic imaging. The study involves a retrospective phase for training the AI model and a prospective phase to validate its diagnostic accuracy compared to standard human assessment.

The study is a multicenter observational non-profit study with a total expected duration of 36 months. It aims to address the challenges in diagnosing CD and NCEs, specifically the subjective nature of endoscopic evaluation and inter-observer variability.

The study proceeds in two phases:

1. Model Development (Retrospective): Training of Deep Learning algorithms on anonymized endoscopic images/videos to identify mucosal atrophy and other lesions (e.g., angiodysplasia, ulcers, polyps).
2. Validation (Prospective): Enrolling patients undergoing small bowel endoscopy to validate the AI system's performance.

The system aims to provide analysis to assist endoscopists, reducing missed lesions and improving diagnostic accuracy.

Validation of the AI system will be performed offline on recorded anonymized endoscopy videos collected prospectively during the validation phase.

Inclusion Criteria:

• Adult patients (age ≥18 years).
• Patients undergoing endoscopic investigation of the small bowel (endoscopy or capsule endoscopy)

Exclusion Criteria:

• Inability to provide informed consent.

Patients undergoing endoscopic investigation of the small bowel (endoscopy or capsule endoscopy) for suspected or known celiac disease, non-celiac enteropathies or other small bowel conditions