Interaction of spectroscopy and artificial intelligence for serological analysis and its applications

Abstract

A spectroscopy and artificial intelligence-interaction serum analysis method includes: collecting bulk SERS spectral data of clinical serum samples, performing dimension reduction on the spectral data by using a covariance matrix to obtain spectral different peak positions of cancer patients and normal individuals, and performing spectral data processing and algorithm identification by using an svm model of an artificial intelligence algorithm to obtain a cancer identification rate. Compared with the conventional serum analysis method, the spectroscopy and artificial intelligence-interaction serum analysis method requires no antibody-antigen or other biological specificity modification processes, and the serum of cancer patients and normal individuals can be identified more cheaply, rapidly and accurately. Also the different peak positions in SERS spectra of a large amount of serum samples can be located, which provides an entirely novel detection and analysis method at a molecular bond energy level for the field of liquid biopsy of clinical cancers.