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
Sex
ALL
Min age
18 Years
Max age
80 Years
Healthy volunteers
No
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
Primary outcome measure(s)
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model — 1 month Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of choledocholithiasis.
Trial sites (1)
Facility
City
Region
Status
Asian Institute of Gastroenterology
Hyderabad
Telangana
Recruiting
More Asian Institute of Gastroenterology, India trials in India
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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