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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\u2013Arnold network (KAN)\u2010enabled metasurface chip (metaEVchip) for ultrasensitive small extracellular vesicles (sEV) analysis in serum is proposed. By analyzing full\u2010spectrum data from 600 pancreatic ductal adenocarcinoma (PDAC) patients and 1200 controls via KAN\u2010powered 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 enhanced performance reveals KAN's mechanism for the simultaneous capture of multi\u2010dimensional spectral features, an advantage that enables efficient data processing and accuracy. This advancement significantly expands the applicability of nanoplasmonic metasurfaces in biosensing and establishes a new paradigm for cancer screening and improved clinical management of multiple malignancies.<\/jats:p>","DOI":"10.1002\/advs.202511337","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T10:42:36Z","timestamp":1758624156000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Deep Learning\u2010Powered Nanoplasmonic Biosensing Approach Enables Ultrasensitive Extracellular Vesicles Profiling for Cancer Screening"],"prefix":"10.1002","volume":"13","author":[{"given":"Jiaheng","family":"Zhu","sequence":"first","affiliation":[{"name":"Institute of Electromagnetics and Acoustics and Key Laboratory of Electromagnetic Wave Science and Detection Technology Xiamen University  Xiamen 361005 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