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1 August 2021 Medical urine analysis method based on Vis-NIR optical spectroscopy using machine learning algorithms.
Igor S. Balashov, Yuri M. Poimanov, Mikhail V. Egorenkov, Ivan A. Pavleev, Pavel P. Nesmiyanov, Larisa M. Samokhodskaya, Andrey A. Grunin, Andrey A. Fedyanin
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Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041W (2021) https://doi.org/10.1117/12.2594715
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States
Abstract
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.
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Igor S. Balashov, Yuri M. Poimanov, Mikhail V. Egorenkov, Ivan A. Pavleev, Pavel P. Nesmiyanov, Larisa M. Samokhodskaya, Andrey A. Grunin, and Andrey A. Fedyanin "Medical urine analysis method based on Vis-NIR optical spectroscopy using machine learning algorithms.", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041W (1 August 2021); https://doi.org/10.1117/12.2594715
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KEYWORDS
Machine learning
Evolutionary algorithms
Optical spectroscopy
Diagnostics
Artificial intelligence
Organisms
Prototyping
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