Application of Artificial Intelligence and Machine Learning in Surface Plasmon Resonance Sensor for Blood Sample Diagnosis

In recent years, various optical biosensor technologies are used by researchers to evaluate the conformational changes of biomolecules and their molecular interactions in a wide range of biomedical diagnostic and analysis operations. One of the most widely used techniques among several optical biosensors is surface plasmon resonance biosensors, which are used for label-free, real-time monitoring with exceptional accuracy and precision. In this study, quick and extremely sensitive SPR biosensors based on artificial intelligence (AI) programmed and machine learning (ML) are proposed to optimize the concentration of hemoglobin, plasma, and platelets in blood cells. Prism N-FK51A, graphene, nickel, potassium niobate, silver metal, and glass make up the suggested SPR biosensor device. Kretschmann configuration is the basis for the device structure, and attenuated total reflection (ATR) is the basis for the device function. Blood samples have undergone numerical analysis of the performance characteristics, including angular sensitivity, quality factor, detection accuracy, limit of detection, and electric field. The suggested surface plasmon resonance biosensor can be used for blood sample diagnosis, which opens the new path in biomedical domain.