Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis

Launched by ASIAN INSTITUTE OF GASTROENTEROLOGY, INDIA · Sep 27, 2023

Keywords

ClinConnect Summary

This clinical trial is studying how machine learning can help doctors determine if certain patients really need special tests called EUS (Endoscopic Ultrasound) or MRCP (Magnetic Resonance Cholangiopancreatography) to check for bile duct stones, a condition known as choledocholithiasis. Many patients fall into a category called "intermediate likelihood," meaning they might have this condition, but it’s not certain. By using machine learning, researchers hope to better identify which patients truly need these tests, potentially reducing unnecessary procedures for some individuals.

To be eligible for the trial, participants must be 18 years or older and have symptoms suggesting choledocholithiasis that meet specific risk criteria. However, people with certain other diseases affecting the pancreas and bile ducts, those with chronic liver disease, pregnant or breastfeeding women, and those who have had their gallbladder removed before cannot participate. If you join the study, you can expect to undergo assessments to see if the machine learning model can accurately help guide your care—ultimately aiming to make the diagnosis process easier and safer for you.

Gender

ALL

Eligibility 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