A ghost in the machine: is machine learning necessary for prediction of choledocholithiasis?

Endoscopy 2024; 56(03): 172-173
DOI: 10.1055/a-2233-0136

Editorial

Referring to Steinway SN et al. doi: 10.1055/a-2174-0534

Ryan Law

¹Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, United States

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²Gastroenterology Department, Hospital Universitario Rio Hortega, Valladolid, Spain

References
1 Steinway SN, Tang B, Telezing J. et al. A machine learning-based choledocholithiasis prediction tool to improve ERCP decision making: a proof-of-concept study. Endoscopy 2024; 56
2 Yu CY, Roth N, Jani N. et al. Dynamic liver test patterns do not predict bile duct stones. Surg Endosc 2019; 33: 3300-3313
3 Suarez AL, LaBarre NT, Cotton PB. et al. An assessment of existing risk stratification guidelines for the evaluation of patients with suspected choledocholithiasis. Surg Endosc 2016; 30: 4613-4618
4 Tranter SE, Thompson MH. Spontaneous passage of bile duct stones: frequency of occurrence and relation to clinical presentation. Ann R Coll Surg Engl 2003; 85: 174-177
5 Beyer G, Kasprowicz F, Hanneman A. et al. 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. Gut 2023; 72: 1738-1744