Deep Learning-Powered Nanoplasmonic Biosensing Approach Enables Ultrasensitive Extracellular Vesicles Profiling for Cancer Screening.

ABSTRACT: Nanoplasmonic metasurface technology, known for its high sensitivity, has garnered significant attention in the field of cancer detection. However, its potential is currently hindered by the inefficient data processing and analysis of conventional biosensing approaches. Herein, a biosensing strategy based on the Kolmogorov-Arnold network (KAN)-enabled metasurface chip (metaEVchip) for ultrasensitive small extracellular vesicles (sEV) analysis in serum is proposed. By analyzing full-spectrum data from 600 pancreatic ductal adenocarcinoma (PDAC) patients and 1200 controls via KAN-powered deep learning nanoplasmonic biosensing, the strategy achieves an exceptional area under the curve (AUC) of 0.99 in an external validation set, outperforming traditional methods. Further exploration of this e

SUBMITTER: Zhu J 

PROVIDER: S-EPMC12786282 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature