An observational study in Choledocholithiasis, sponsored by Asian Institute of Gastroenterology, India. Recruiting at 1 site in India. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2026-01-06.
Sponsored by Asian Institute of Gastroenterology, India · Observational
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.
109 studies on the registry are indexed under Choledocholithiasis; 24 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 200 across 31 observational studies indexed under Choledocholithiasis.
Browse Choledocholithiasis studies →Asian Institute of Gastroenterology, India is the lead sponsor of 135 studies on the registry; 59 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Cancer (malignancy)
Malignancy means cancer. Criteria about another or earlier malignancy ask whether you have had a different cancer, usually within a set number of years.
Some are commonly allowed, such as treated skin cancers other than melanoma and early cancers removed completely — check the exceptions this study lists.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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
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
Started Oct 2023 (actual). Primary completion is estimated for Jun 2026, 32 months after the start.
How these figures are worked out, and which studies are left out of them.
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Asian Institute of Gastroenterology, India →