Choledocholithiasis (Gallstones in Bile) Prediction Algorithm iOS App



A mobile app to predict gallstones in the bile duct in children.

Key Benefits

  • Accurate, non-invasive prediction of gallstones in the bile duct.
  • Based on readily available patient information.

Market Summary

Choledocholithiasis or bile stones in the gal duct is one of the most common medical conditions leading to surgical intervention. In the past several decades, the incidence rate of cholelithiasis, especially in children, has increased, leading to surgical treatment via cholecystectomy. Endoscopic retrograde cholangiopancreatography (ERCP) is an effective therapeutic option for stone extraction from the biliary tree, but it is an invasive endoscopic procedure, and its role and timing have been subject to debate. Unfortunately, no accurate non-invasive diagnostics methods are available, leading to unnecessary surgical procedures for those without stones. Therefore, new non-invasive diagnostic methods are needed to reduce or eliminate surgical procedures in those without choledocholithiasis.

Technical Summary

Researchers have developed a novel prediction model for pediatric choledocholithiasis, an effective therapeutic for gall stone extraction from the bile duct in children. The model utilizes a combination of demographic variables, initial serum laboratory values, and ultrasound imaging results into its algorithm for determining the necessity of pediatric surgery and demonstrated an overall accuracy of 71.52% when tested using full patient datasets. Four independent variables were identified to have a significant presence with common bile duct in children those being alanine aminotransferase, common bile duct diameter, total bilirubin, and alkaline phosphatase. The inventors incorporated this algorithm into a mobile app allowing for the prediction model to be more easily utilized by physicians treating pediatric patients at risk of common bile stone ducts.

Developmental Stage

A beta version of the app has been created.

Publication: Cohen, R. Z., Tian, H., Sauer, C. G., Willingham, F. F., Santore, M. T., Mei, Y., & Freeman, A. J. (2021). Creation of a Pediatric Choledocholithiasis Prediction Model. Journal of Pediatric Gastroenterology and Nutrition, 73(5). doi:10.1097/MPG.0000000000003219

Patent Information

Tech ID: 20221
Published: 5/13/2022