Early-Stage Disease Detection via Intravascular Nanomachines: Modeling and Analysis

Abstract

Early-stage disease detection remains challenging due to the low abundance of circulating biomarkers during the initial stages of disease. Among emerging technologies, intra-body nanoscale communication offers an innovative solution to identify molecular cues within the human bloodstream. This study investigates a minimally invasive approach for early-stage disease detection using nanomachines introduced into the bloodstream. To assess the feasibility of this approach, computational simulations are used to emulate the vascular environment and evaluate biomarker detection performance under different physiological conditions. Current modeling approaches often fail to capture essential vascular characteristics, including non-uniform flow structures, size-dependent particle mobility, and particle margination driven by red blood cell interactions. To address these limitations, our study incorporates these factors into the simulation framework and quantifies their individual and combined effects on biomarker detection probability. Baseline detection performance is first obtained under uniform flow assumptions, after which introducing selected vascular transport mechanisms progressively reduces detection probability for all vessel types and nanomachine sizes. Among the considered vessels, capillary-scale vessels provide the most favorable local encounter conditions across all nanomachine sizes. These findings highlight the need to include vessel-dependent transport effects when evaluating nanomachine-enabled biomarker detection in microvascular environments.