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
• Status: Not yet recruiting
• 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
• 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
• 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

Related Conditions & MeSH terms

• Choledocholithiasis

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