Artificial Intelligence vs Endoscopist Identification in EUS Normal Anatomy

Comparative Evaluation of Artificial Intelligence and Endoscopists´ Accuracy in Endoscopic Ultrasound for Identifying Normal Anatomical Structures: A Multi-institutional, Cross-sectional Study

Endoscopic ultrasound (EUS) visual impression is operator-dependant and can hinder diagnostic accuracy, especially in less experienced endoscopists. The implementation of artificial intelligence can potentially mitigate operator dependency and interpretation variability, helping or improving the overall accuracy.

The investigators therefore aim to compare diagnostic accuracy between artificial intelligence (AI)-based model and the endoscopists when identifying normal anatomical structures in EUS-procedures.

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.