Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.
None
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
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
Case-only
Prospective
Not listed
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.
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.
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.
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