AIMC Topic: Glycosuria

Clear Filters Showing 1 to 3 of 3 articles

Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising non-invasive approach for diagnosing glycosuria, a condition characterized by excess sugar in the urine of diabetic patients. This study investigated...

Non-invasive Characterization of Glycosuria and Identification of Biomarkers in Diabetic Urine Using Fluorescence Spectroscopy and Machine Learning Algorithm.

Journal of fluorescence
The current study presents a steadfast, simple, and efficient approach for the non-invasive determination of glycosuria of diabetes mellitus using fluorescence spectroscopy. A Xenon arc lamp emitting light in the range of 200-950 nm was used as an ex...

First Laboratory Evaluation of FUS-3000 Plus: A New-Generation Urine Analyzer.

The journal of applied laboratory medicine
BACKGROUND: Urine sediment analysis is a cornerstone of diagnostic testing. This study evaluates FUS-3000 Plus, an automated urine sediment analyzer using advanced imaging and artificial intelligence, to assess its technical performance and diagnosti...