A ghost in the machine: is machine learning necessary for prediction of choledocholithiasis?
Author:
Affiliation:
1. Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, United States
2. Gastroenterology Department, Hospital Universitario Rio Hortega, Valladolid, Spain
Publisher
Georg Thieme Verlag KG
Link
http://www.thieme-connect.de/products/ejournals/pdf/10.1055/a-2233-0136.pdf
Reference5 articles.
1. A machine learning-based choledocholithiasis prediction tool to improve ERCP decision making: a proof-of-concept study;SN Steinway;Endoscopy,2024
2. Dynamic liver test patterns do not predict bile duct stones;CY Yu;Surg Endosc,2019
3. An assessment of existing risk stratification guidelines for the evaluation of patients with suspected choledocholithiasis;AL Suarez;Surg Endosc,2016
4. Spontaneous passage of bile duct stones: frequency of occurrence and relation to clinical presentation;SE Tranter;Ann R Coll Surg Engl,2003
5. Definition of age-dependent reference values for the diameter of the common bile duct and pancreatic duct on MRCP: a population-based, cross-sectional cohort study;G Beyer;Gut,2023
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