Artificial Intelligence in Endoscopic Ultrasound
Purpose
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.
Conditions
- Pancreatic Cancer
- Pancreas Disease
- Pancreatic Cyst
Eligibility
- Eligible Ages
- Between 18 Years and 100 Years
- Eligible Sex
- All
- Accepts Healthy Volunteers
- No
Inclusion Criteria
- Age ≥ 18 years - Any patient undergoing endoscopic ultrasound examination
Exclusion Criteria
- Age < 18 years
Study Design
- Phase
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Arm Groups
| Arm | Description | Assigned Intervention |
|---|---|---|
| Patients undergoing endoscopic ultrasound procedures | Patients will undergo endoscopic ultrasound procedures as planned. Abnormalities in the pancreas identified by the endoscopist during the endoscopic ultrasound examination will be correlated against those detected by the AI platform. |
|
Recruiting Locations
Orlando, Florida 32806
More Details
- Status
- Recruiting
- Sponsor
- Orlando Health, Inc.
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.