#AI reads Urine# Is It Time to Consider Population-Based Urine Dipstick Screening for Early Detection of Kidney Disease?

This article focuses on whether population-based urine dipstick screening should be implemented for the early detection of kidney disease. It points out that there is a large number of kidney disease patients worldwide with a heavy economic burden. Urine dipstick testing, featuring rapidity, low cost, and non-invasiveness, can detect various components such as protein and blood in urine, making it applicable in multiple scenarios. However, it also has limitations including false positives, false negatives, and insufficient sensitivity in detecting early microalbuminuria, leading to inconsistent conclusions from relevant studies. Meanwhile, technological innovations like machine learning and smartphone applications are expected to enhance its effectiveness. The large-scale implementation of this screening method needs to comprehensively consider factors such as healthcare system capacity and treatment accessibility, and it may be of great significance for high-risk groups in low- and middle-income countries.

Kidney Int Rep. 2025 Oct 24;11(1):1-5. doi: 10.1016/j.ekir.2025.10.013.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/