ИСТИНА

In a current study, we have developed a cheap and easy-to-use urine analysis method using visible and near-infrared wavelength range optical transmission spectra using artificial intelligence approaches. The manufactured prototype based on an 18-channel spectrometer and LED light sources, was used to measure 431 patients’ urine transmission spectra. 19 parameters clinical urine analysis was performed in a medical laboratory for each patient. Machine learning partial least squares discriminant analysis (PLS-DA) was used to solve the binary multidimensional classification problem. Developed machine learning model could detect urine pathological changes with sensitivity and specificity comparable to laboratory diagnostic methods for most parameters.