# Deep Learning‐Powered Nanoplasmonic Biosensing Approach Enables Ultrasensitive Extracellular Vesicles Profiling for Cancer Screening

## TL;DR

A new biosensing platform using deep learning and nanoplasmonic technology can detect cancer with high accuracy by analyzing tiny cell fragments in blood.

## Contribution

A deep learning framework using Kolmogorov–Arnold networks improves nanoplasmonic biosensing for ultrasensitive extracellular vesicle profiling.

## Key findings

- The biosensing strategy achieved an AUC of 0.99 in detecting pancreatic cancer from serum samples.
- KAN enables efficient processing of multi-dimensional spectral data for improved accuracy.
- The method outperforms traditional biosensing approaches in 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 enhanced performance reveals KAN's mechanism for the simultaneous capture of multi‐dimensional 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.

This study presents a Kolmogorov–Arnold network (KAN)‐powered nanoplasmonic biosensing platform for ultrasensitive detection of small extracellular vesicles in serum, enabling highly accurate pancreatic cancer screening. The method achieves an area under the curve (AUC) of 0.99 in a large clinical cohort, offering a robust and interpretable deep learning framework for non‐invasive cancer diagnostics.