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

Sponsor

Asian Institute of Gastroenterology, India (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT06066372

Collaborator

(none)

1,000

Enrollment

Anticipated Duration (Months)

Study Details

Study Description

Brief 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.

Condition or Disease	Intervention/Treatment	Phase
Choledocholithiasis

Detailed 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.

Study Design

Study Type:

Observational

Anticipated Enrollment :

1000 participants

Observational Model:

Case-Only

Time Perspective:

Prospective

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

Anticipated Study Start Date :

Oct 1, 2023

Anticipated Primary Completion Date :

Oct 30, 2024

Anticipated Study Completion Date :

Oct 30, 2024

Outcome Measures

Primary Outcome Measures

Development of machine learning algorithm to predict choledocholithiasis [One year]
develop machine learning algorithm to predict choledocholithiasis in patients with intermediate likelihood of choledocholithiasis

Secondary Outcome Measures

1. Compare diagnostic accuracy of EUS or MRCP with machine learning algorithm [One year]
1. Compare diagnostic accuracy of EUS or MRCP with machine learning algorithm
2. To validate machine learning algorithm [One Year]

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 80 Years

Sexes Eligible for Study:

All

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

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

Asian Institute of Gastroenterology, India

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Asian Institute of Gastroenterology, India

ClinicalTrials.gov Identifier:

NCT06066372

Other Study ID Numbers:

AI EUS Choledocholithiasis

First Posted:

Oct 4, 2023

Last Update Posted:

Oct 4, 2023

Last Verified:

Sep 1, 2023

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Additional relevant MeSH terms:

Choledocholithiasis

Study Results

No Results Posted as of Oct 4, 2023