The invention provides a glomerular anomaly
recognition system and method based on Raman spectrum and
artificial intelligence, and relates to the field of biosensing and
artificial intelligence, and the method comprises the following steps: collecting and pretreating real-time Raman spectrum of glomerular related biomolecular components in a blood or
urine sample; extracting a key spectrum peak and an intensity characteristic of the Raman spectrum; comparing the extracted key spectrum peak and the intensity characteristic thereof with the key spectrum peak and the intensity characteristic thereof of the normal glomerulus to obtain a difference characteristic; and constructing a glomerular anomaly identification model, and identifying the glomerular anomaly condition by taking the difference distinguishing features as input features of the glomerular anomaly identification model. According to the glomerular anomaly
recognition system and method based on the Raman spectrum and the
artificial intelligence, accuracy, real-time performance and noninvasive performance of glomerular
disease diagnosis are achieved through spectrum collection hardware optimization, AI
algorithm innovation, clinical tool integration and safety protocol deployment.