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

Asian Institute of Gastroenterology, India1,000 enrolled

Overview

Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.

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

1,000

Conditions

Choledocholithiasis

Eligibility

Sex: ALLMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

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

Locations (1)

Asian Institute of Gastroenterology

Hyderabad, Telangana, India

RECRUITING

Outcomes

Primary Outcomes

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.

Time frame: 1 month

Secondary Outcomes

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.

Time frame: 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.

Time frame: 1 Month

Central Contacts

Nitin G Jagtap, MD

CONTACT

+919182859523 docnits13@gmail.com

Hardik Rughwani, MD

CONTACT

+919182859523 hardik.hr@gmail.com

Data from ClinicalTrials.gov

This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.