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NCT Number: NCT06564571

Artificial Intelligence in Endoscopic Ultrasound

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.

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Key information

Conditions

Pancreatic Cancer Cysts Digestive System Diseases Digestive System Neoplasms Endocrine Gland Neoplasms Endocrine System Diseases Neoplasms Neoplasms by Site Pancreas Disease Pancreatic Cyst Pancreatic Diseases Pancreatic Neoplasms

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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

Exclusion criteria

  • Age < 18 years

Treatment and study plan

Patients undergoing endoscopic ultrasound procedures

Device

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.

Primary outcomes

  1. Rate of detection pancreatic abnormalities by AI

    Time frame: 1 day

    Ability of AI to detect pancreatic abnormalities as identified by an endoscopist during EUS examination of the pancreas.

Secondary outcomes

  1. Rate of detection pancreatic solid mass lesions by AI

    Time frame: 1 day

    Ability of AI to detect pancreatic solid mass lesions as identified by an endoscopist during EUS examination of the pancreas.

  2. Rate of detection pancreatic cystic lesions by AI

    Time frame: 1 day

    Ability of AI to detect pancreatic cystic lesions as identified by an endoscopist during EUS examination of the pancreas.

Study contacts

Contact information is provided by the study sponsor or research team.

Barbara Broome

CONTACT

[email protected]

321-841-4356

Shyam Varadarajulu, MD

CONTACT

[email protected]

321-841-2431

Sponsors and collaborators

Lead sponsor

Orlando Health, Inc.

Other

Registry information

Important dates

Study start
2024
Primary completion
2026
Study completion
2027
First posted
Aug 21, 2024
Registry last updated
May 31, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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