Prospective Evaluation of an Endoscopic Ultrasound (EUS)-Based Artificial Intelligence to Assist in the Deployment of Lumen Apposing Metal Stents for Gallbladder Drainage

Lumen apposing metal stents are now being used to help patients who suffer from cholecystitis (infection of the gallbladder), especially in cases where patients are not candidates for surgery. Lumen apposing metal stents are effective for draining the gallbladder, however, placement is technically challenging. Scientists have developed an artificial intelligence to aid doctors in the deployment of these stents into the gallbladder. The aim of this study is test the performance of an artificial intelligence in providing physicians accurate information for gallbladder drainage.

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Key information

Conditions

Cholecystitis Biliary Tract Diseases Digestive System Diseases Gallbladder Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

UMass Chan Medical School/Umass Memorial Health Care

Worcester, Massachusetts, 01655, United States

Who can participate

Healthy volunteers accepted: Yes

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

Inclusion criteria

Age ≥ 18 years old
Anticipation that patient may undergo endoscopic ultrasound
Gallbladder present

Exclusion criteria

Cholecystectomy
No indication for endoscopic ultrasound

Treatment and study plan

Primary outcomes

Accuracy of the EUS AI system in providing a recommendation (i.e. whether it is safe or not to drain the gallbladder)

Time frame: Day 1

An expert endoscopist will perform the endoscopic ultrasound procedure. When the gallbladder comes into view the expert will comment as to whether they believe that the gallbladder is safe to drain. The expert will be blinded to the AI computers interpretation of the view of the gallbladder. A second observer will record what the AI recommended at the time of the blinded expert. The expert will be considered the "gold standard" and the outcome will be accuracy of the AI in mimicking the experts recommendations.

Sponsors and collaborators

Lead sponsor

University of Massachusetts, Worcester

Other

Registry information

Acronym: EUS-AI GBD

Important dates

Study start: 2025
Primary completion: 2025
Study completion: 2025
First posted: May 20, 2025
Registry last updated: Mar 17, 2026

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.

Published trials that share one or more normalized conditions with this study.

Completed