Artificial Intelligence in Endoscopic Ultrasound

Recruiting

• Conditions: Pancreas Disease; Pancreatic Cancer; Pancreatic Cyst

• Registration Number: NCT06564571
• Lead Sponsor: Orlando Health, Inc.

Brief Summary

The objective of the study is to determine if this artificial intelligence system is capable of detecting abnormalities in the pancreas that are identified by an endoscopist at endoscopic ultrasound procedures.

Detailed Description

Endoscopic Ultrasound (EUS) is an equipment where an ultrasound transducer is attached to the tip of the endoscope. When advanced to the stomach the organs outside such as the pancreas and liver can be visualized in great detail. This enables diagnosis of conditions such as pancreatic cancer. However, an endoscopist must undergo training to accurately interpret these ultrasound images.

The investigators are in the process of developing an artificial intelligence system that could potentially interpret EUS images. The objective of the study is to determine if this artificial intelligence system is capable of detecting abnormalities in the pancreas that are identified by an endoscopist at endoscopic ultrasound procedures. Such correlation if established will lead to possible development of an artificial intelligence platform that can diagnose pancreatic diseases. Such development will potentially minimize human error and decrease learning curve to gain proficiency in EUS.

Study Timeline

• Study Start: 2024-01-19
• Status Verified: 2024-08
• Study First Submitted: 2024-08-19
• Study First Submitted QC: 2024-08-19
• Study First Posted: 2024-08-21
• Last Update Submitted: 2025-05-27
• Last Update Posted: 2025-05-31
• Primary Completion: 2026-12
• Study Completion: 2027-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 310

Inclusion Criteria

• Age ≥ 18 years
• Any patient undergoing endoscopic ultrasound examination

Exclusion Criteria

• Age < 18 years

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Rate of detection pancreatic abnormalities by AI	1 day	Ability of AI to detect pancreatic abnormalities as identified by an endoscopist during EUS examination of the pancreas.

Secondary Outcome Measures

Name	Time	Method
Rate of detection pancreatic solid mass lesions by AI	1 day	Ability of AI to detect pancreatic solid mass lesions as identified by an endoscopist during EUS examination of the pancreas.
Rate of detection pancreatic cystic lesions by AI	1 day	Ability of AI to detect pancreatic cystic lesions as identified by an endoscopist during EUS examination of the pancreas.

Trial Locations

Locations (1)

Orlando Health; 🇺🇸 Orlando, Florida, United States