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

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 Overview

• Status: Recruiting
• Conditions: 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 Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Nitin G Jagtap, MD; Phone Number: +919182859523; Email: docnits13@gmail.com
• Study Contact Backup: Name: Hardik Rughwani, MD; Phone Number: +919182859523; Email: hardik.hr@gmail.com

Study Locations

• India
  • Telangana
    • Hyderabad, Telangana, India, 500032
      • Recruiting
      • Asian Institute Of Gastroenterology
      • Contact: Mohan Ramchandani, MD; Phone Number: +919282859523; Email: docnits13@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Probability Sample

Study Population

All patients with suspected choledocholithiasis satisfying either ESGE or ASGE risk stratification criteria of intermediate likelihood of choledocholithiasis will be prospectively enrolled from AIG Hospitals, Hyderabad

Description

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 Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model	Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of choledocholithiasis.	1 month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic Accuracy Metrics of Endoscopic Ultrasound (EUS) or Magnetic Resonance Cholangiopancreatography (MRCP)	Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC) of magnetic resonance cholangiopancreatography (MRCP) for identification of choledocholithiasis.	1 Month
Validation Performance of the Machine Learning Prediction Model	Validation performance of the machine learning model for predicting choledocholithiasis, assessed using AUROC, calibration metrics (Brier score), and calibration plots in an independent validation cohort.	1 Month

Collaborators and Investigators

• Sponsor: Asian Institute of Gastroenterology, India
• Investigators: Study Director: Mohan Ramchandani, MD, Asian Institute Of Gastroenterology

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2023
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): October 30, 2026

Study Registration Dates

• First Submitted: September 13, 2023
• First Submitted That Met QC Criteria: September 27, 2023
• First Posted (Actual): October 4, 2023

Study Record Updates

• Last Update Posted (Actual): January 6, 2026
• Last Update Submitted That Met QC Criteria: January 4, 2026
• Last Verified: December 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases; Biliary Tract Diseases; Bile Duct Diseases; Cholelithiasis; Common Bile Duct Diseases; Choledocholithiasis
• Other Study ID Numbers: AI EUS Choledocholithiasis

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No