Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Choledocholithiasis. Basic parameters: 18 years — 80 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- India
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official 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
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.
Detailed description
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.
Primary outcome measures
- Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model [Time frame: 1 month]
Secondary outcome measures (2)
- Diagnostic Accuracy Metrics of Endoscopic Ultrasound (EUS) or Magnetic Resonance Cholangiopancreatography (MRCP) [Time frame: 1 Month]
- Validation Performance of the Machine Learning Prediction Model [Time frame: 1 Month]
Eligibility criteria
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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Study design
- Observational model
- Case-only
Study locations
India · 1 center
- Asian Institute of Gastroenterology — Hyderabad
Identifiers
NCT: NCT06066372 · AI EUS Choledocholithiasis