Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patien
Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.
| Condition(s) | Choledocholithiasis |
|---|---|
| Status | Recruiting |
| Study type | Observational |
| Summary | 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 | 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 |
| Ages | 18 Years to 80 Years |
| Sex | All |
| Lead sponsor | Asian Institute of Gastroenterology, India |
| Locations | Hyderabad, Telangana, India |
| Start date | 2023-10-01 |
| NCT ID | NCT06066372 |
| Official listing | https://clinicaltrials.gov/study/NCT06066372 |