Optoplasmonic Biosensor for Lung Cancer Telemedicine: Classical and Quantum Approach

Abstract

Recently, the healthcare industry has faced significant pressure due to the factors such as population growth, escalating medical equipment costs, and the rising prevalence of complex diseases. As a result, there has been a notable shift towards electronic consulting, telediagnosis, and remote treatment as means of overcoming such physical constraints. In this research, the classical and quantum approaches were developed for lung cancer telemedicine. The classical methods utilized to investigate the impact of noble metallic nanosurface (NMNS) thickness and biomolecule concentration on the performance of optoplasmonic biosensor (OPB). The result indicated that, the nonlinear intensity have more broader spectra than linear, ab sorption cross section increase with size of NMNS, scattering cross section have inversely proportional with NMNS size, and silver nanosurface (Ag NS) exhibits more robust plasmonic properties than gold and aluminum NS. Notably, the ad dition of eight layers of Ag NS yields optimum sensitivity (10421 nm/RIU), and increasing biomolecule concentration leads to increase signal response. In addi tion, the lung cancer biosignals extracted from the OPB are correlated with patient information in the cloud using quantum machine learning (QML) techniques, to determine the appropriate doses of laser interstitial thermal therapy (LITT). The findings reveal that, as the quality and quantity of lung cancer clinical data (LCCD) improved, the accuracy of diagnosis increases. Moreover, through the utilization of quantum teleportation techniques, the biosignals and LITT are successfully tele ported between two remote health stations with fidelity rates of 96% for biosignals and 98% for LITT doses. This shows the proposed telemedicine schemes is anticipated solutions for long-distance faithful lung cancer telemedicine.