Machine learning to reduce extra imaging (EUS or MRCP) for people with suspected common bile duct stones
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
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 type | Observational |
|---|---|
| Enrollment | 1000 (estimated) |
| Ages | 18 Years to 80 Years |
| Sex | All |
| Sponsor | Asian Institute of Gastroenterology, India Academic / other |
| Locations | 1 site (Hyderabad, Telangana) |
| Trial ID | NCT06066372 on ClinicalTrials.gov |
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.
Who should consider this trial
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.
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.
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 performed: 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.
Eligibility criteria
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 this trial is running
Hyderabad, Telangana
- Asian Institute of Gastroenterology — Hyderabad, Telangana, India (Recruiting)
Study contacts
- Study coordinator: Nitin G Jagtap, MD
- Email: docnits13@gmail.com
- Phone: +919182859523
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.