RecruitingNCT06066372

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

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

Sponsor

Asian Institute of Gastroenterology, India

Enrollment

1,000 participants

Start Date

Oct 1, 2023

Study Type

OBSERVATIONAL

Conditions

Choledocholithiasis

Summary

Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.

Eligibility

Min Age: 18 YearsMax Age: 80 Years

Inclusion Criteria1

• 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 Criteria4

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

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Locations(1)

Asian Institute of Gastroenterology

Hyderabad, Telangana, India

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NCT06066372

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