Actively Recruiting
Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis
Led by Asian Institute of Gastroenterology, India · Updated on 2026-01-06
1000
Participants Needed
1
Research Sites
160 weeks
Total Duration
On this page
AI-Summary
What this Trial Is About
Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.
CONDITIONS
Official Title
Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Adults aged 18 years or older up to 80 years
- Suspected choledocholithiasis with intermediate likelihood according to ASGE or ESGE criteria
- Planned to undergo EUS or MRCP for diagnosis
You will not qualify if you...
- Presence of other pancreatobiliary diseases such as chronic pancreatitis, biliary stricture, pancreatobiliary malignancy, or portal biliopathy
- Existing chronic liver diseases
- Pregnancy or breastfeeding
- Previous gallbladder removal (cholecystectomy)
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 1 location
1
Asian Institute of Gastroenterology
Hyderabad, Telangana, India, 500032
Actively Recruiting
Research Team
N
Nitin G Jagtap, MD
CONTACT
H
Hardik Rughwani, MD
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
0
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