Portable Raman Spectroscopy Offers Cost-Effective Kidney Disease Diagnosis at POC

While these tests are common, they can face challenges, including complex laboratory processes and unstable biomarkers. Traditional approaches are expensive and time-consuming, delaying detection and treatment. Researchers have now developed a portable, non-invasive diagnostic method using urine samples for detecting kidney disease.

A team of researchers from the Polytechnic University of Lisbon (IPL, Lisbon, Portugal), the Center of Technology and Systems (UNINOVA-CTS, Caparica, Portugal), and collaborators, has developed a portable Raman spectroscopy system to make kidney disease diagnostics more accessible. Drawing on the OpenRAMAN project’s “Starter Edition” methodology, they optimized the system through careful calibration. Adjustments included laser temperature control, emission spectrum evaluation, and ethanol-based acquisition tuning, enabling the device to capture subtle variations in urine composition.

System validation was performed on five urine samples, demonstrating sensitive and consistent spectral detection. A unique aspect of this study was the use of artificial intelligence (AI): a neural network trained on methanol and ethanol spectra classified samples with 99.19% accuracy and 99.21% precision in just three minutes. The integration of AI highlights the system’s potential to automate spectral analysis and support diagnostic decision-making. Findings were published in Sensors.

The portable system offers significant cost advantages over traditional Raman instruments, which can cost tens of thousands of euros. In contrast, the device could be priced below five thousand euros, making it feasible for clinical use, emergency care, and deployment in remote areas. This affordability, combined with portability, positions it as a promising tool for point-of-care diagnostics.

Challenges remain, including high noise levels and low peak intensity in acquired spectra. Researchers suggest that higher-power lasers could improve clarity by reducing fluorescence interference. Future work will expand testing on larger datasets of healthy and diseased patient samples to identify biomarkers linked to kidney disease. Plans also include refining optical elements to improve sensitivity and performance.