The Dirty Nappy Study

This observational study evaluates whether a machine-learning algorithm, a computer program that learns patterns from data, can accurately diagnose cholestasis in newborns. Cholestasis refers to reduced or blocked bile flow from the liver, which can lead to liver damage. A severe form of cholestasis is biliary atresia, a condition where the bile ducts are damaged or absent, requiring early treatment to prevent long-term harm.

The study involves infants from birth, both healthy and those potentially affected by cholestasis, recruited from four UK hospitals. It addresses two primary aims:

* Accuracy of Diagnosis: Can the machine-learning algorithm accurately identify cholestasis and biliary atresia using parent-provided stool images? This will be assessed by measuring sensitivity (the ability to correctly detect true cases) and specificity (the ability to correctly identify infants without the condition). * Feasibility of Screening: Is using parent-provided images a feasible and acceptable screening method for early detection?

To evaluate these aims, researchers will compare two groups:

* Infants with abnormal stool images who are subsequently diagnosed with cholestasis or biliary atresia. * Infants with normal stool images who do not develop biliary atresia.

This comparison will help determine the algorithm's ability to distinguish between infants with and without these conditions.

Parents will:

* Take smartphone photos of their baby's dirty diapers at 14, 21, and 28 days of age. * Upload the images for analysis by the algorithm. * Provide feedback on their experience with this screening process.

The study seeks to determine if parent-submitted stool images can serve as a practical early screening tool for cholestasis, potentially enabling faster diagnosis and improved outcomes for affected infants.