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NCT06066372 Clinical Trial

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

Choledocholithiasis Clinical Trial

Official title:
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

Clinical Trial Details — Status: Not yet recruiting

Administrative data
NCT number NCT06066372
Other study ID # AI EUS Choledocholithiasis
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date October 1, 2023
Est. completion date October 30, 2024

Study information
Verified date September 2023
Source Asian Institute of Gastroenterology, India
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.

Description:

The current guidelines for suspected choledocholithiasis are aimed to reduce the risk of patient receiving diagnostic ERCP and reduce the risk of post ERCP adverse events. In this process there is apparent increase in number of patients in the intermediate likelihood group requiring EUS or MRCP. This can increase the health care utilization and cost of care for intermediate likelihood patients. The field of artificial intelligence in clinical medicine is evolving rapidly. The use of artificial intelligence based machine learning model is not adequately studied for prediction of choledocholithiasis. Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.

Recruitment information / eligibility
Status Not yet recruiting
Enrollment 1000
Est. completion date October 30, 2024
Est. primary completion date October 30, 2024
Accepts healthy volunteers
Gender All
Age group 18 Years to 80 Years
Eligibility 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

Study Design

Related Conditions & MeSH terms

Locations
Country Name City State
n/a

Sponsors (1)
Lead Sponsor Collaborator
Asian Institute of Gastroenterology, India

Outcome
Type Measure Description Time frame Safety issue
Primary Development of machine learning algorithm to predict choledocholithiasis develop machine learning algorithm to predict choledocholithiasis in patients with intermediate likelihood of choledocholithiasis One year
Secondary 1. Compare diagnostic accuracy of EUS or MRCP with machine learning algorithm 1. Compare diagnostic accuracy of EUS or MRCP with machine learning algorithm One year
Secondary 2. To validate machine learning algorithm One Year
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