Pancreas Disease, Pancreatic Cancer, Pancreatic Cyst
Conditions
Keywords
pancreatic disease, pancreatic cancer, pancreatic cyst, artificial intelligence
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.
Interventions
DEVICEPatients 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.
Sponsors
Orlando Health, Inc.
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No
Inclusion criteria
* Age ≥ 18 years * Any patient undergoing endoscopic ultrasound examination
Exclusion criteria
* Age \< 18 years
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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. |
Countries
United States
Contacts
Primary ContactShyam Varadarajulu, MD
Backup ContactBarbara Broome
Outcome results
None listed