Clinical trial Recruiting at 1 site Observational Ages 18 to 80

Machine learning to reduce extra imaging (EUS or MRCP) for people with suspected common bile duct stones

Official title: Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis- A Prospective, Open Label, Diagnostic Study

Run by Asian Institute of Gastroenterology, India · NCT06066372 · Data updated 2026-11-12

This project will test whether a machine-learning tool can identify which adults with an intermediate chance of common bile duct stones can safely skip EUS or MRCP.

Quick facts

Study typeObservational
Ages18 Years to 80 Years
SexAll
Enrollment1000 (estimated)
SponsorAsian Institute of Gastroenterology, India
Academic / other
Locations1 site (Hyderabad, Telangana)
StatusRecruiting
StartedOctober 1, 2023
Main results expectedJune 30, 2026
Study endsOctober 30, 2026
Registry record updatedJanuary 6, 2026
Trial IDNCT06066372 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Adults (≥18 years) with suspected common bile duct stones who meet ASGE or ESGE intermediate-risk criteria and are scheduled for EUS or MRCP, without other pancreatobiliary disease, chronic liver disease, pregnancy or prior cholecystectomy.

Probably not a fit

Patients with high or low pretest probability, those with other pancreatobiliary conditions, chronic liver disease, pregnancy, prior cholecystectomy, or those unable to undergo standard imaging are unlikely to benefit from this model.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational project will use clinical data from adults classified as intermediate likelihood for choledocholithiasis to train and test machine-learning models that predict the presence of common bile duct stones. Participants meeting ASGE or ESGE intermediate-risk criteria who undergo EUS or MRCP at the Asian Institute of Gastroenterology will be included, with exclusions for other pancreatobiliary disease, chronic liver disease, pregnancy or breastfeeding, and prior cholecystectomy. Model predictions will be compared to imaging results to determine whether a subgroup can be identified who do not require EUS or MRCP. The goal is to reduce unnecessary advanced imaging, associated costs, and procedure-related risks if the model proves accurate.

Why it matters

Potential benefit: If successful, the tool could let some patients avoid EUS or MRCP, reducing costs, wait times, and procedure-related risks.

How similar studies have gone: Machine learning specifically for predicting choledocholithiasis is relatively novel with limited published evidence, though AI methods have shown promise in related diagnostic and risk-stratification tasks.

Who can join: the full rules

Show full inclusion / exclusion criteria
Inclusion Criteria:

• Individual 18 years or older with a suspected choledocholithiasis satisfying either ASGE or ESGE risk stratification criteria of intermediate likelihood undergoing EUS or MRCP

Exclusion Criteria:

* Patients having co-exiting disease of pancreato biliary system other than gall stones and choledocholithiasis which include chronic pancreatitis, biliary stricture, pancreatobiliary malignancy, portal biliopathy
* Patients having underlying chronic liver diseases
* Pregnancy and breast feeding
* Previous history of cholecystectomy

Where it's running

  • Asian Institute of Gastroenterology
    Hyderabad, Telangana, India
    Recruiting

Study contacts

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.