#AI reads Urine#Interpretable Multiwavelength SERS Fingerprints of Human Urine for Ischemic Stroke Diagnosis.
Published 20 January, 2026
This document introduces a new method for diagnosing ischemic stroke using multi-wavelength surface-enhanced Raman spectroscopy combined with artificial intelligence. Ischemic stroke is highly harmful, and traditional diagnostic techniques have limitations such as low sensitivity and invasiveness. Urine, as a non-invasive and easily accessible body fluid, can reflect the disease condition through changes in its metabolic substances. The researchers collected urine samples from 29 healthy individuals and 34 patients with ischemic stroke, dropped the urine onto a surface-enhanced Raman spectroscopy chip with a silver-coated gold nanostructure, collected spectral data using two wavelengths of lasers (532 nm and 633 nm), and stacked the data vertically. After preprocessing and enhancement of the data, a convolutional neural network model was used for analysis. This model achieved a diagnostic accuracy of 85% and an area under the curve of 92.8%. Additionally, through an algorithm, the spectral ranges crucial for diagnosis were identified, which correspond to the characteristic signals of biomolecules related to stroke. This method has the advantages of short detection time, low cost, and no risk of cross-infection. However, the current sample size is relatively small; in the future, more samples will be needed for verification, and exploration of combining it with other medical data will be required to improve its applicability and accuracy.
ACS Sens. 2025 Dec 3. doi: 10.1021/acssensors.5c03213.
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.
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