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NCT Number: NCT06066372

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

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Key information

Conditions

Choledocholithiasis Bile Duct Diseases Biliary Tract Diseases Cholelithiasis Common Bile Duct Diseases Digestive System Diseases

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Asian Institute of Gastroenterology

Hyderabad, Telangana, 500032, India

Location status: Recruiting

Location contact

Mohan Ramchandani, MD

CONTACT

[email protected]

+919282859523

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model

    Time frame: 1 month

    Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of choledocholithiasis.

Secondary outcomes

  1. Diagnostic Accuracy Metrics of Endoscopic Ultrasound (EUS) or Magnetic Resonance Cholangiopancreatography (MRCP)

    Time frame: 1 Month

    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.

  2. Validation Performance of the Machine Learning Prediction Model

    Time frame: 1 Month

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Hardik Rughwani, MD

CONTACT

[email protected]

+919182859523

Nitin G Jagtap, MD

CONTACT

[email protected]

+919182859523

Sponsors and collaborators

Lead sponsor

Asian Institute of Gastroenterology, India

Other

Registry information

Official study 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

Important dates

Study start
2023
Primary completion
2026
Study completion
2026
First posted
Oct 4, 2023
Registry last updated
Jan 6, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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