Photonic Crystal-Based Water Concentration Estimation in Blood Using Machine Learning for Identification of the Haematological Disorder

Abstract

Human blood is made up primarily of water. Water is significantly involved in balancing the human body. It affects the component of blood like mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and mean platelets volume (MPV). The water concentration varies from 80 to 90% in blood. The change in water concentration changes the refractive index of plasma, and the change in the refractive index of plasma also changes the refractive index of blood. The proposed structure is designed to analyze the water concentration in human blood by analyzing the shifting in resonant peak and this shifting is processed by machine learning algorithm to estimate the concentration of water in human blood. Nanocavity ring structures in the waveguide region are designed as sensing nodes in this proposed structure. The air hole radius of these Nanocavity ring structures is 80 and 50 nm, whereas the proposed structure’s dimension is 12.15 by 8.45 μm2. The sensitivity of the design structure is 570 nm/RIU, and the quality factor is 650. The structure is simulated through the Finite Difference Time Domain (FDTD) method.