Artificial Intelligence vs Endoscopist Identification in EUS Normal Anatomy

EUS is an operator dependent procedure where accuracy depends on experience and skills. Nowadays, EUS-training can be achieved by a formal fellowship training in a center for 6-24 months or an informal training through didactic sessions with a short hands-on experience. However, parameters for a correct and complete learning experience measurement are yet to be defined. The implementation of artificial intelligence on EUS can potentially mitigate the operator-dependent variable and improve diagnostic accuracy.

Therefore, detection of normal anatomical structures on a separate basis using an AI-based model, expert and non-expert endoscopists to determine where the AI would be most helpful.

The investigators aim to compare the diagnostic accuracy of the AI-based model with the endoscopists identification of normal anatomical structures in EUS procedures.

Inclusion criteria

• Expert gastrointestinal EUS-endoscopists.
• Non-expert gastrointestinal endoscopists training for EUS.
• Patients with chronic dyspepsia without other findings.
• Patients with previous CT images or upper digestive endoscopy reporting no other findings.
• Patients requiring EUS for surveillance due to family history of pancreatic cancer without findings on MRI.

Exclusion criteria

• Internet connection less than 100 MBs per second.
• Patients with abnormal structures or with visible lesions.