The Dirty Nappy Study

Recruiting now

Conditions studied: Cholestasis in Newborn, Biliary Atresia, Neonatal Cholestasis

In brief

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.

Key facts

Study ID
NCT07697872
Run by
Birmingham Women's and Children's NHS Foundation Trust
People needed
5350
Starts
2025-04-01
Expected to finish
2026-09-30
Last updated by the study team
2026-07-13

Who can join

Age: any, up to 11. Sex: any. Healthy volunteers: accepted.

You may qualify if…

You may not qualify if…

Where it is running

Full record on ClinicalTrials.gov

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