#AI reads Urine# Conjugated-Polymer-Based Electronic Tongue for Breast Cancer Discrimination: from Artificial to Clinical Urine Samples

Summary

This page introduces an electronic tongue based on conjugated polymers for distinguishing urine samples from breast cancer patients and healthy individuals. The electronic tongue analyzes metabolic differences in urine samples and converts them into electrochemical fingerprints, which are then classified using various machine learning models. The study results show that this method has good distinguishing capabilities in both artificial urine samples and clinical urine samples.

Key Points

• An electronic tongue is constructed using sensors modified with conjugated polymers.
• Electrochemical fingerprints are used to identify urine samples from breast cancer patients and healthy individuals.
• Various machine learning models are employed for classification, including PCA, PLS-DA, and gradient boosting.
• In artificial urine samples, the gradient boosting model achieves a test accuracy of 96%.
• In clinical urine samples, the gradient boosting model yields a 97% accuracy.

Anal Chem. 2026 Feb 2. doi: 10.1021/acs.analchem.5c07243.

Youhe Gao

Statement: During the preparation of this work the author(s) used GLM-WEB / 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/